Autism, attention-deficit/hyperactivity disorder (ADHD), and related neurodivergent presentations are increasingly being identified and assessed in adulthood. However, validated, accessible, and bias-conscious assessment tools remain limited [1,2]. Most existing instruments were originally developed for children or rely primarily on single-source self-report, reducing their usefulness for adults who have developed sophisticated compensatory strategies, particularly women and gender-diverse individuals [3,4]. For example, the Ritvo Autism Asperger Diagnostic Scale–14 Screen (RAADS-14) showed adequate sensitivity (95%) but poor specificity (70%) when validated outside its original population, suggesting that single-source screening instruments may lack the discriminative precision needed for culturally and linguistically diverse adult populations [5]. Social camouflaging, or masking, is associated with increased anxiety, depression, exhaustion, burnout, and delayed identification [6-8]. Rather than representing merely a source of measurement error, masking is itself a clinically meaningful phenomenon that assessment systems should detect, preserve, and interpret.

Contemporary digital assessment platforms increasingly integrate multiple sources of evidence, including self-report questionnaires, informant reports, structured performance tasks, clinician-observed behavioral tasks, semistructured interview observations, response-time telemetry, and open-ended responses. Although this multimodal approach has the potential to improve assessment, these sources frequently yield discrepant findings. Self- and informant reports may diverge because informants observe individuals across different contexts [9]. Likewise, performance- or observation-based measures may underestimate traits that are successfully masked in structured settings, whereas unusual response patterns may reflect fatigue, alexithymia, or demand characteristics rather than the absence of neurodivergent traits. When these heterogeneous signals are collapsed into opaque composite scores, clinically meaningful discrepancies may be obscured, leading to overconfident interpretations and reduced transparency.

This creates a recurring clinical challenge. In adult neurodivergence consultations—particularly among late-diagnosed adults, women, and gender-diverse individuals—the primary clinical bottleneck is rarely a scarcity of questionnaires or metrics, but rather the interpretation of legitimate discrepancies between information sources. For instance, an adult patient may report severe internal masking effort, cognitive exhaustion, and sensory overload, while a workplace colleague or parent informant describes seemingly adaptive, functional outward behavior. Crucially, conventional psychometric practice frequently resolves cross-source discrepancy by calculating an arithmetic mean or composite score across informants. From a neuroaffirmative clinical standpoint, arithmetic averaging across sources is fundamentally flawed: it incorrectly assumes that self- and informant reports measure the identical construct under identical environmental conditions; it collapses the vital distinction between subjective internal cost and observable external appearance; and it treats discrepancy as mere “measurement error” to be averaged away, thereby invalidating the lived experience of the evaluated individual.

In the absence of transparent integration logic, clinicians are often forced either to favor a single source of evidence or to disregard discrepancies altogether. The SPECTRA (Spectrum & Clinical Tools for Reliable Assessment) Evidence Stack was developed to address this gap by treating discrepancies not as noise to be resolved, but as interpretable, traceable, and clinically meaningful signals. Under the SPECTRA framework, cross-source discordance opens a structured investigative hypothesis—prompting targeted clinical inquiry into compensation history, environmental scaffolding, or contextual variance—rather than closing inquiry with a premature, automated diagnostic verdict. It is intended as a clinician-facing decision-support module that structures multisource assessment data within a clinical workflow, not as a stand-alone diagnostic system. In the intended workflow, a qualified clinician reviews the SPECTRA output (evidence-strength band, reason codes, and next-step recommendations) during a multidisciplinary assessment or clinical formulation meeting; the clinician decides whether additional history, informant clarification, or reassessment is needed and can override or defer the output when it is inconsistent with the broader clinical context, following a standardized override, deferral, and audit protocol (Methods section). Training requirements for users will be evaluated in future usability studies.

A neuroaffirmative assessment framework—one that respects neurodivergent identity and experience rather than treating difference as deficit—should regard discrepancies between sources as potentially informative rather than automatically invalidating. Differences across self-report, informant-report, and performance-based evidence may reflect masking (self-report lower than informant or performance measures), hypervigilance or heightened self-monitoring (self-report higher than informant measures), context-dependent trait expression, limited self-insight, or state-dependent variability in functioning. Each pattern has distinct clinical implications and therefore warrants explicit interpretation rather than forced convergence.

This perspective is consistent with growing calls for transparency and explainability in clinical decision-support systems [10-12]. It also responds to longstanding critiques from the autistic community regarding deficit-oriented frameworks and opaque scoring methods [13-16], as well as participatory research approaches that emphasize the involvement of autistic individuals in the design and evaluation of assessment tools [17,18]. Within this framework, identity-first, nonpathologizing language and explicit decision logic are not merely stylistic preferences; they are methodological requirements for transparent and trustworthy assessment.

Existing approaches to multisource evidence in digital assessment typically fall into 2 categories: opaque composite scores that collapse discrepancies into a single metric, and machine learning models that optimize prediction at the cost of interpretability [10,11]. Neither approach is well suited to neurodivergence assessment, where disagreement across sources is often clinically meaningful and where autistic communities have emphasized the importance of transparent, explainable decision-making [13-16]. The SPECTRA Evidence Stack differs from these approaches in 3 ways. First, unlike interpretable early-warning scores or sepsis-alert systems that combine multiple data sources into a single risk score, SPECTRA preserves discrepancy as a structured convergence profile rather than resolving it into one number. Second, unlike multi-informant autism instruments that report source discrepancies descriptively, SPECTRA feeds discrepancy profiles directly into an evidence-strength estimate with mandatory reason codes and next-step recommendations. Third, unlike black-box predictive models, SPECTRA makes every output traceable to explicit data conditions and predefined rules, with a hard nondiagnostic boundary enforced by output contracts.

At this formative stage, a rule-based architecture was intentionally prioritized over machine learning because interpretability, auditability, and regulatory transparency were considered more important than predictive optimization. This prioritization is consistent with emerging regulatory expectations for AI-enabled clinical decision support, which emphasize transparent documentation, human oversight, and postmarket monitoring [19]. Rule-based systems have well-known limitations: they cannot easily capture complex, nonlinear interactions unless those interactions are explicitly encoded, and their performance depends heavily on the completeness and accuracy of the specified rules. However, in neurodivergence assessment, disagreement across information sources is often clinically meaningful rather than undesirable, and autistic communities have consistently emphasized the importance of transparent decision-making. Under these conditions, an entirely black-box model would limit interpretability at precisely the stage where transparency is most critical. A rule-based approach therefore trades some predictive flexibility for accountability and explainability.

Accordingly, the SPECTRA Evidence Stack adopts a rule-based architecture because it provides 3 properties that are essential for an early-stage assessment framework: (1) interpretability—every output can be traced to explicit data conditions and predefined decision rules, enabling clinicians and respondents to understand why a particular confidence estimate, convergence profile, or reason code was generated; (2) auditability—the integration logic of the reference configuration can be independently inspected, challenged, and refined without retraining opaque model parameters, thereby supporting iterative improvement through clinical, community, and regulatory review; and (3) regulatory appropriateness—rule-based clinical decision-support systems are generally easier to document, audit, and evaluate than autonomous diagnostic algorithms, making them well suited to emerging expectations for transparency in AI-enabled health technologies [10,20].

The current reference configuration was refined during revision in response to editorial and reviewer comments. The parameter values used for the final reported execution were frozen before that execution and recorded in the accompanying implementation. They were selected through an expert-informed design process led by a psychiatrist with expertise in adult neurodevelopmental assessment and reviewed against the framework’s design principles—transparency, uncertainty preservation, and conservative evidence integration—by the coauthors. They were not preregistered or empirically optimized against clinical outcomes and are provisional rather than optimal; empirical calibration is reserved for future validation studies. Accordingly, the principal contribution of this paper lies in the framework architecture and explicit decision logic rather than in the numerical values assigned to individual parameters.

Importantly, this rule-first strategy should not be interpreted as a rejection of machine learning. If future empirical studies demonstrate that learned components improve calibration while maintaining interpretability and auditability, they could be incorporated within the existing architectural framework without altering its layer contracts or transparency principles.

This multistudy report presents 3 interrelated methodological evaluations of the SPECTRA Evidence Stack. First, in study 1 (architecture specification), we describe the SPECTRA Evidence Stack, a 3-layer framework for integrating heterogeneous evidence in adult neurodivergence assessment, specifying transparent and traceable decision pathways linking raw assessment inputs to quality flags, convergence profiles, confidence estimates, reason codes, and next-step recommendations. Second, in study 2 (synthetic rule-configuration comparison), we compare 4 deliberately specified rule configurations on 250 author-generated synthetic records (seed=42) to verify their documented behavior under varying configurations of evidence quality, missingness, and cross-source discrepancy. Configurations B and C are comparators, not controlled knockouts, and their differences from A are not causal estimates of the isolated effects of a layer. Third, in study 3 (sensitivity analyses and clinical vignettes), we evaluate (1) threshold-perturbation sensitivity across 12 scenarios (±10% perturbation on 6 quality thresholds); (2) an extended parameter-sensitivity analysis covering all 22 reference-configuration parameters one at a time, a global joint perturbation (Latin hypercube, 1000 samples, ±20%) with PRCC indices, and tipping-point sweeps of the 3 confidence-band cutoffs; and (3) a set of 3 worked clinical demonstration vignettes (masking-compatible divergence, informant-context mismatch, and sparse evidence) with full decision traces.

These studies collectively support the development of neuroaffirmative, transparent, and clinically accountable assessment technologies. The proprietary implementation, calibration assets, and software components of the SPECTRA platform remain protected intellectual property of the developing organization.

The present study represents an early-stage clinical decision-support framework development phase. A DECIDE-AI (Developmental and Exploratory Clinical Investigations of Decision Support Systems Using Artificial Intelligence) reporting checklist [20] is provided as a prospective alignment—a roadmap for the framework’s future clinical evaluation—rather than as a record of a clinical evaluation that has already been performed. No clinical validation or effectiveness study is reported here. No formal preregistration was performed because the work is an early-stage methodological specification and synthetic stress test rather than a confirmatory hypothesis-testing study.

Equally important is defining the boundaries of this work. This paper does not evaluate diagnostic validity against independent clinical adjudication, report instrument-specific psychometric properties, or make claims regarding clinical effectiveness or real-world utility. Those questions will be addressed in subsequent publications within the SPECTRA research program, including validation studies of the Autism Understanding by Relative Assessment (AURA), Neurodivergent Expression Assessment (NEXA), Veiling Effort & Identity Load (VEIL), and Textual Recognition of Pragmatic Expressions (TROPE) instruments; a test-retest reliability study of Autism Spectrum Test–10 Items (TEA-10); and a capstone clinical validation study comparing Evidence Stack outputs with independent expert adjudication. Establishing these scope boundaries is essential to prevent premature claims of validity and to emphasize the formative nature of the present work.

The methods reported in this paper reflect principles of transparent reporting for measurement frameworks and decision-support tools [20,22,23]. We provide explicit architectural documentation, decision rules, parameter values, and rule-configuration logic to enable conceptual replication and critical evaluation. Although the present work involves a synthetic stress test rather than a clinical validation study, we address key transparency elements: (1) description of the framework and its components (Study Design: The SPECTRA Evidence Stack; Layer 1: Response Quality; Layer 2: Multisource Convergence; and Layer 3: Confidence Synthesis sections); (2) user population and setting (Introduction and Design Principles and Scope sections); (3) data inputs, quality, and governance (Input Schema and Layer 1: Response Quality sections); (4) logic, algorithms, and rules (Layer 1: Response Quality, Layer 2: Multisource Convergence, and Layer 3: Confidence Synthesis sections); (5) outputs and their presentation to users (Layer 3: Confidence Synthesis and Study 3 Results: Clinical Demonstration Vignettes sections); (6) human oversight (Design Principles and Scope; Clinician Override, Deferral, and Audit Protocol; and Ethical Considerations sections); (7) ethical safeguards (Ethical Considerations section); and (8) limitations and next steps (Discussion section). Future clinical validation studies will follow the COSMIN (Consensus-based Standards for the selection of health Measurement Instruments) guidelines for evaluating measurement properties [23] and will report psychometric performance (criterion validity, test-retest reliability, and measurement invariance) alongside clinical utility end points.

This manuscript reports a methods description and a synthetic computational evaluation. No human participants were involved in the empirical component, and therefore no research ethics committee (Comitê de Ética em Pesquisa/Comissão Nacional de Ética em Pesquisa [CEP/CONEP]) approval was required for the work reported here, consistent with the scope of Brazilian National Health Council (Conselho Nacional de Saúde) Resolution 510/16, which exempts research that does not involve human participants or identifiable human data. The SPECTRA platform operates under an institutional umbrella research ethics approval held by the NeurAtip/SPECTRA program (Research Ethics Committee of Universidade Christus [Unichristus], CAAE 95653025.2.0000.5049, Parecer 8.397.639, approved April 30, 2026), which governs the platform-wide assessment studies—including the instrument-validation papers for NEXA, VEIL, TEIA, and others—that will provide the real clinical data referenced by this architecture. The present paper reports no participant data under that approval and cites it only as institutional context. The community and clinician input described above was informal, non-systematic consultation—individuals were approached directly for design feedback and lived-experience perspectives, and this activity did not involve systematic collection or analysis of identifiable opinions; it therefore did not constitute human-subjects research and required no ethics determination. Subsequent validation studies that involve human data collection will be submitted for review by a research ethics committee via Plataforma Brasil (CEP/CONEP) before initiation, in accordance with National Health Council (Conselho Nacional de Saúde) Resolutions 466/12 and 510/16 and the Declaration of Helsinki.

The framework incorporates the following safeguards, which govern any future deployment. Informed consent requires independent electronic consent for all data sources, with respondents and informants permitted to withdraw at any time. Data minimization ensures that only data necessary for the specified assessment purpose are collected. Pseudonymization requires identifiers to be separated from responses, with linkage restricted to authorized access. Quality flags trigger support actions rather than negative labels, ensuring that no punitive flags are generated. Human-in-the-loop oversight requires all outputs to be reviewed by a qualified clinician before incorporation into clinical interpretation. The clinician examines confidence bands, reason codes, quality flags, and next-step recommendations; determines whether additional history, informant clarification, or reassessment is needed; and can override or defer the output when it is inconsistent with the broader clinical context. LGPD and GDPR compliance is supported by consent as the legal basis for data processing and encryption of data at rest and in transit.

Foreseeable harms and inappropriate-use scenarios include the possibility that, because the convergence profiles carry clinically suggestive labels (eg, “masking-compatible,” “hypervigilance-compatible”), the framework could, if used without qualified clinical oversight or as a stand-alone decision tool, anchor interpretation, pathologize a respondent, or be mistaken for a diagnostic result. The framework is therefore explicitly designed as decision support within a clinical workflow and is not authorized for stand-alone diagnostic use, for high-stakes determinations (eg, legal, occupational, or custody decisions), or for any use without review by a qualified clinician. The nondiagnostic boundary is enforced by output contracts and reason-code wording, and the next-step recommendations are restricted to evidence-gathering and assessment-process guidance.

Override documentation will follow the intended workflow, in which the reviewing clinician may override or defer the framework’s output when it is inconsistent with the broader clinical context. Overrides and deferrals follow the standardized Clinician Override, Deferral, and Audit Protocol subsection (Methods), which specifies decision types, mandatory deferral triggers, minimum documentation fields, automation-bias safeguards, and a quarterly audit rubric with prespecified action thresholds; the full rubric and log schema are provided in Multimedia Appendix 1. Safety performance, including the rate and appropriateness of overrides, the frequency of clinician disagreement with outputs, and any adverse consequences of use, will be formally evaluated in the planned usability and clinical validation studies before any deployment.

The formative evaluation used synthetic data and therefore cannot speak to demographic fairness. Production calibration will include differential item functioning and measurement invariance analyses across gender, age, education, and neurotype. These analyses are planned for the instrument-specific validation papers in the SPECTRA program.

The rule-configuration results are summarized in Table 8. Under the recorded implementation version and seed, configuration A had the highest mean evidence strength. This ordering is a descriptive consequence of the complete configuration definitions and is not a causal estimate of any isolated layer.

The low dispersion in configuration C reflects the source_coverage gate’s hard ceiling on single-source evidence (Table 3: “single source caps at moderate”), rather than higher measurement precision; consistent with this rule, no configuration C record reaches the high band.

Paired comparisons with configuration A (descriptive contrasts within the fixed synthetic dataset) were constructed as follows. Because the same 250 records were evaluated under each complete configuration, these differences describe configuration-level separation and do not isolate a causal layer effect. The bootstrap intervals describe empirical resampling variability of each statistic within the fixed synthetic cohort, not population uncertainty or numerical stability. The mean difference between configuration A and configuration B (no quality) was 0.07 (paired-difference SD 0.13; 95% bootstrap interval 0.06-0.09). The mean difference between configuration A and configuration C (no convergence) was 0.06 (paired-difference SD 0.15; 95% bootstrap interval 0.05-0.08). The mean difference between configuration A and configuration D (baseline) was 0.11 (paired-difference SD 0.23; 95% bootstrap interval 0.08-0.14).

The bootstrap intervals were computed by resampling the 250 synthetic records with replacement (B=2000), separately for each paired difference, and taking the 2.5th and 97.5th percentiles. We report raw mean differences and paired-difference SDs because this is a seeded comparison of rule-specified outputs on the same fixed records, not an inferential test between independent clinical samples.

We identified the subset of cases with a discordant convergence profile (masking-compatible, hypervigilance-compatible, or context-dependent; n=113, 45.2%). This subset was defined post hoc by the same convergence profiles the framework detects, so the analysis is illustrative rather than confirmatory. In this subset, the full stack produced higher evidence-strength scores than the no-convergence configuration (full-stack mean 0.61, SD 0.10; no-convergence mean 0.54, SD 0.08; raw mean difference 0.07). This pattern is a direct arithmetic consequence of the framework’s design: when sources disagree, the stack’s explicit handling of discrepancy preserves evidence strength rather than collapsing it to the single-source baseline or default cap. The effect is directional and method-level; it demonstrates rule execution rather than an empirical clinical effect size.

The purpose of the full stack is not to maximize evidence strength but to assign it conditionally. In configuration C, no record reached high because the configuration includes a single-source ceiling. Configuration B produced 10% insufficient, 36.4% low, 34.8% moderate, and 18.8% high, with SD 0.21. Configuration D had SD 0.20 and no interpretive layer structure. These patterns describe the four complete configurations. Because configuration B and configuration C also change penalties, jitter, source coverage, or caps, the contrasts do not identify the causal effect of removing a single layer.

These findings are formative and synthetic. They demonstrate intended rule behavior, structural consistency (ie, outputs change monotonically and traceably with changes in inputs and layers), and traceability within the disclosed reference configuration, not clinical validity.

To make the aggregate rule-configuration results traceable at the record level, Table 9 reports mean evidence strength for the full-stack (A) and no-quality (B) configurations, stratified by the record’s quality state (clean, caution, or critical). The quality state reflects the count and severity of activated quality flags in the full-stack configuration.

Across the 250 records, the full-stack configuration produced higher mean confidence than the no-quality configuration within the clean (0.63 vs 0.52) and caution (0.60 vs 0.55) states. In the critical state (n=8), the full stack constrained confidence substantially (0.19), whereas comparison configuration B did not discount these degraded records (0.50) because that configuration does not use the response quality layer. This record-level pattern is the mechanism behind the aggregate finding that configuration B (no quality) produced the largest combined insufficient + low proportion (46.4%) and the highest dispersion (SD 0.21) of any configuration, and it illustrates the intended conditional behavior: degraded records are discounted only when quality gating is active.

Textbox 1 summarizes what this paper evaluates and what it does not evaluate. This firewall protects against premature validity claims and aligns with the SPECTRA publication program.

The main contribution of this paper is methodological: it presents a formally specified and neuroaffirmative framework for integrating heterogeneous assessment signals in adult neurodivergence assessment; traceability is demonstrated within the disclosed reference configuration. By separating response quality, multisource convergence, and confidence synthesis into explicit layers, the SPECTRA Evidence Stack makes integration logic inspectable and disputable. By treating discrepancy as structured signal rather than noise, it aligns assessment technology with contemporary clinical and community understanding of masking and context-dependent expression.

The synthetic stress test was consistent with the intended behavior: the framework assigns evidence strength conditionally rather than maximizing it. In the rule-configuration comparison, configuration A had a mean of 0.60 (SD 0.17), configuration C had a mean of 0.54 (SD 0.07), and configuration D had a mean of 0.50 (SD 0.20). These are descriptive outputs of complete, deliberately specified configurations rather than causal layer effects or findings of clinical superiority. The separation observed among discordance-sensitive cases (n=113, 45.2%) is likewise descriptive. Because the synthetic records were constructed to contain the patterns the framework detects, the findings show rule traceability and programmatic fidelity rather than independent empirical validation. They demonstrate internal consistency and seeded reproducibility within the disclosed reference configuration; they do not establish diagnostic validity or comparative effectiveness.

By providing reason codes and next-step recommendations, the framework is designed to support shared decision-making and reduce the risk that a single low-quality or single-source score drives clinical conclusions. These are design affordances demonstrated at the architectural level; whether they actually facilitate shared decision-making in clinical practice is a question for future usability and implementation studies.

Digital phenotyping and mobile health studies in psychiatry often prioritize signal capture and predictive modeling rather than transparent handling of quality or discrepancy across multiple sources of evidence [25]. Some clinical assessment systems include validity flags, but these are often framed as respondent-level judgments and may not feed into confidence estimates. The SPECTRA Evidence Stack extends this work by (1) making quality flags data conditions rather than person judgments, (2) preserving multisource discrepancy as interpretable profiles, and (3) generating mandatory reason codes and next-step recommendations. The novel contribution, as a design specification, lies in combining these 3 elements within a single neuroaffirmative, nondiagnostic architecture for adult neurodivergence assessment, where discrepancy is treated as clinically meaningful signal rather than noise to be averaged away. This design is closer to explainable clinical decision-support systems—such as rule-based sepsis alerts or interpretable early-warning scores that combine multiple data sources into structured, auditable outputs—than to traditional test-scoring pipelines. It also aligns with the broader regulatory principle that transparent documentation and human oversight should be established before claiming clinical utility for AI-enabled clinical decision support [19]. Notably, multi-informant assessment methods have gained traction even in affect-regulation domains: the Alexithymia Questionnaire for Children–Parent version (AQC-P) demonstrated that parent-reported and self-reported alexithymia measures yield meaningful but imperfect concordance, with measurement invariance supported across gender [26]. This finding resonates with the SPECTRA framework’s treatment of discrepancy as interpretable signal, suggesting that cross-informant divergence in alexithymia assessment—a feature relevant to autistic populations—should be preserved rather than averaged away.

The primary value of the SPECTRA Evidence Stack for digital health assessment lies not in providing an automated, closed diagnostic classifier, but in rendering clinical reasoning and assessment uncertainty explicit, structured, and auditable. In conventional digital assessment, the synthesis of conflicting clinical signals typically occurs either inside opaque machine-learning models or through unstandardized clinician heuristics. By contrast, the formative contribution of this architecture is to make visible the precise conditions under which evidence converges or diverges, providing an objective scaffolding that supports rather than supplants clinical expertise.

To maintain ethical and clinical integrity, the framework incorporates 5 foundational human-in-the-loop safeguards. First, uncertainty preservation over forced certainty is maintained. When input data are degraded, missing, or contradictory, the system systematically discounts confidence rather than manufacturing false precision. Second, rule-specified suspension of reader-facing interpretation is applied. Under a critical incomplete session or high missingness, the stack retains a technical score and band for QA but suppresses both values in the reader-facing output. It emits the applicable reason code and displays “Insufficient — human review required”; human review is an action, not a reason code. Third, traceable, nonblack-box scoring is ensured. Fixed transformations and reason-code selection follow published rules, while synthetic generation and score jitter are stochastic and reproducible conditional on the recorded seed and implementation version. Fourth, interpretive draft status is maintained. All generated outputs—including convergence profiles, confidence bands, reason codes, and next-step suggestions—constitute a structured interpretive draft. Sovereign diagnostic judgment and clinical formulation remain exclusively with the licensed health care professional who reviews, contextualizes, and signs the final evaluation. Fifth, standardized override, deferral, and audit are implemented. Divergence between output and clinical judgment is handled by a prespecified protocol—4 decision types, mandatory deferral triggers, minimum documentation fields, explicit automation-bias safeguards (including blind initial impression and mandatory reason-code reading), and a quarterly audit rubric with action thresholds that trigger recalibration—rather than left to unstandardized clinician heuristics (Methods section; Multimedia Appendix 1).

Importantly, the formative clinical experience and operational insights that informed this architecture highlight recurring challenges in adult neurodevelopmental assessment that the framework was engineered to make structured and observable. The vignettes, archetypes, and decision pathways presented here are formative and illustrative; they do not constitute a clinical trial, nor do they establish empirical diagnostic efficacy. To translate this architecture into robust clinical workflows, future prospective research must systematically evaluate clinician usability, cognitive load, user comprehension, and informational safety across real-world practice settings.

This study has important limitations. The evaluation used synthetic data constructed to contain the patterns the architecture detects, so the rule-configuration comparison is an internal consistency check rather than an independent test. Because the synthetic cases were intentionally constructed to represent framework-relevant scenarios, they should not be interpreted as approximations of clinical prevalence distributions or real-world patient populations. The synthetic dataset, generation algorithm, and analysis script for the reference configuration are deposited at OSF (code component [21]) to enable independent replication of the numerical results. The architectural rules and reference-configuration parameters are fully specified in this paper and the accompanying code. Flag thresholds, convergence tolerances, and evidence-strength weights were refined during revision through a structured expert-informed process. The final values were frozen before the reported execution but were not globally preregistered or established before peer review. This process was led by a psychiatrist with expertise in adult neurodevelopmental assessment and reviewed by the coauthors, with additional consultative input acknowledged below. The band cutoffs and quality thresholds require empirical calibration; a prespecified calibration protocol (data split, blinded reference standard, calibration metrics, constrained optimization, and freeze and acceptance criteria) is now provided in the Methods section and will be executed in the clinical validation study, following established measurement-property standards such as COSMIN [23]. The extended sensitivity analysis identified the caution cap and the high-band cutoff as the parameters with the largest influence on outputs; these will be prioritized in calibration. The joint-perturbation results also show that the configuration ordering is not invariant under large simultaneous parameter moves (preserved in 27.8% of ±20% samples), reinforcing that the reported ordering is a property of the reference configuration rather than a universal law. Relatedly, the response quality metrics used in the present framework rely on multiitem composites; brief or single-item measures—which have been identified as a pervasive but underscrutinized practice in psychological assessment [27]—were deliberately avoided to preserve measurement precision across the response quality, multisource convergence, and confidence synthesis layers. The framework has not been tested beyond its source language, and the neuroaffirmative framing has not yet been evaluated with autistic users. Furthermore, the multisource architecture presupposes the availability of informant reports and performance-based measures, which may be challenging in resource-constrained public health settings where informant availability is limited and structured performance tasks are not routinely administered. The feasibility and cost-effectiveness of deploying the full evidence stack in such contexts remain unexamined. Diagnostic accuracy, clinical utility, and subgroup fairness are reserved for subsequent studies. No formal preregistration was performed for this formative framework-development study; the study was designed as an early-stage methodological specification and synthetic stress test rather than a confirmatory hypothesis-testing study.

The two most urgent next steps are: (1) a clinical validation study comparing evidence stack outputs with independent gold-standard diagnostic adjudication performed by clinicians blinded to all battery and evidence stack outputs, reported in accordance with DECIDE-AI and the Standards for Reporting of Diagnostic Accuracy Studies (STARD) guidelines; and (2) usability studies with clinicians and autistic adults to evaluate the interpretability of convergence profiles, reason codes, and next-step recommendations, as well as the training requirements for intended users. The blinded adjudication procedure is essential to prevent incorporation bias: if the adjudicating clinician has access to the instrument scores or evidence stack output, the reference standard is not independent and accuracy estimates become uninterpretable. Clinical validation is prioritized because the framework’s intended value depends on whether its outputs correspond with expert clinical judgment obtained under blinded conditions; usability studies will then determine whether the outputs are interpretable and actionable in practice. After these steps, the program will proceed with instrument-specific validation studies of AURA, NEXA, VEIL, and TROPE; longitudinal stability analyses using TEA-10; and fairness and measurement invariance analyses across gender, age, education, and neurotype. Because the stack’s layer contracts separate decision logic from parameter values, future iterations could substitute learned components (for example, machine learning–based item scoring or ensemble adjudication) wherever they demonstrably improve calibration without sacrificing traceability—a path toward explainable, traceable AI-mediated assessment that is the subject of ongoing development and will be reported separately.

The rule logic, parameter values, pseudocode, synthetic dataset, and analysis script that support the findings of this study are deposited at OSF (code component [21]). The Methods section describes how the records were constructed to enable conceptual replication. Online supplementary material is provided with this submission: Multimedia Appendix 2 (DECIDE-AI prospective-alignment checklist) and Multimedia Appendix 1 (clinician override, deferral, and audit protocol).

An earlier version of this manuscript was deposited as a preprint at OSF [24]. The reference configuration and outputs reported here were refined and recomputed during peer review in response to editorial and reviewer comments. The historical preprint record may retain an earlier title or earlier results and should not be read as already containing every revision reported here.

This research was not preregistered. The present work is an early-stage methodological specification and synthetic stress test rather than a confirmatory hypothesis-testing study.

The SPECTRA Evidence Stack offers a transparent, traceable within the disclosed reference configuration, and neuroaffirmative conceptual architecture for multisource adult neurodivergence assessment. It makes uncertainty visible, preserves discrepancy as interpretable evidence, and is designed to support transparent and collaborative clinical decision-making, pending future clinical evaluation. This formative methods paper establishes the architecture and demonstrates conservative, traceable behavior under varying evidence conditions, as summarized in the scope firewall (Textbox 1). It does not establish diagnostic accuracy; that evaluation is reserved for a subsequent clinical validation study. We offer the framework as a contribution to the growing literature on explainable, ethical, and community-informed digital health assessment.