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Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective Study

Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective Study

Univariate analysis was performed using 2-tailed t tests and chi-square tests to identify significant differences between the p CR and non-p CR groups. Features that exhibited statistical significance (P The performance of the model was evaluated using accuracy and the area under the receiver operating characteristic curve (AUROC).

Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang

JMIR Cancer 2025;11:e64685

A Personalized, Texting-Based Conversational Agent to Address Sleep Disturbance in Individuals Who Have Survived Breast Cancer: Protocol for a Pilot Waitlist Randomized Controlled Trial

A Personalized, Texting-Based Conversational Agent to Address Sleep Disturbance in Individuals Who Have Survived Breast Cancer: Protocol for a Pilot Waitlist Randomized Controlled Trial

To determine if group differences are present after randomization, descriptive statistics of baseline characteristics (eg, age, cancer severity, and time since treatment) will be generated, and the treatment and control groups will be compared with t tests for continuous variables and chi-square tests for categorical variables.

Chi-shan Tsai, Warren Szewczyk, Michelle Drerup, Jason Liao, Alexi Vasbinder, Heather Greenlee, Jaimee L Heffner, Rachel Yung, Kerryn W Reding

JMIR Res Protoc 2025;14:e62712

COVID-19–Relevant Insights Into the Elevated Risk of Accidental Injuries in Survivors of SARS and Their Relatives in Taiwan: Retrospective Cohort Study

COVID-19–Relevant Insights Into the Elevated Risk of Accidental Injuries in Survivors of SARS and Their Relatives in Taiwan: Retrospective Cohort Study

Categorical variables were examined with the chi-square test, while continuous variables, expressed as means (SDs), were compared using 2-sample t tests. To evaluate the risk of injuries among patients with and without exposure to patients with SARS or their relatives, competing risk analysis was performed using Fine and Gray’s model to calculate adjusted hazard ratios (AHRs) and 95% CIs, adjusting for sociodemographic characteristics and comorbidities.

Chieh Sung, Chi-Hsiang Chung, Chien-An Sun, Chang-Huei Tsao, Daphne Yih Ng, Tsu-Hsuan Weng, Li-Yun Fann, Fu-Huang Lin, Wu-Chien Chien

JMIR Public Health Surveill 2025;11:e70608