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Implementation Outcomes of Reusable Learning Objects in Health Care Education Across Three Malaysian Universities: Evaluation Using the RE-AIM Framework

Implementation Outcomes of Reusable Learning Objects in Health Care Education Across Three Malaysian Universities: Evaluation Using the RE-AIM Framework

Chen et al [37] reported a similar finding, with a low nonbounced rate of 40% for an undergraduate online course. The wide discrepancy in the completion and nonbounced rates among RLOs may be attributed to the different levels of learning across various learner groups. RLOs integrated into the curriculum as teaching and learning materials achieved a completion rate greater than 70% (RLO 8 and RLO 23), suggesting that learners are more invested in completing these RLOs.

Hooi Min Lim, Chin Hai Teo, Yew Kong Lee, Ping Yein Lee, Kuhan Krishnan, Zahiruddin Fitri Abu Hassan, Phelim Voon Chen Yong, Wei Hsum Yap, Renukha Sellappans, Enna Ayub, Nurhanim Hassan, Sazlina Shariff Ghazali, Nurul Amelina Nasharuddin, Puteri Shanaz Jahn Kassim, Faridah Idris, Klas Karlgren, Natalia Stathakarou, Petter Mordt, Stathis Konstantinidis, Michael Taylor, Cherry Poussa, Heather Wharrad, Chirk Jenn Ng

JMIR Med Educ 2025;11:e63882

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