{"id":"W4408165416","doi":"10.1016/j.clbc.2025.03.002","title":"Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine Biomarkers","year":2025,"lang":"en","type":"article","venue":"Clinical Breast Cancer","topic":"Effects of Radiation Exposure","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; Ontario Institute for Cancer Research; University of Toronto","funders":"Temerty Faculty of Medicine, University of Toronto; Institute of Biomedical Science; Princess Margaret Cancer Foundation; University of Toronto; AstraZeneca","keywords":"Medicine; Breast cancer; Oncology; Internal medicine; Cancer","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009978847,0.0002857508,0.000935197,0.0002463019,0.000149135,0.00001847377,0.00009003697,0.000316837,0.00003460493],"category_scores_gemma":[0.001098249,0.0002468736,0.0002140264,0.0003571754,0.0001258272,0.0002317827,0.00009486823,0.000515829,2.895794e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003579016,"about_ca_system_score_gemma":0.0007146265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001241781,"about_ca_topic_score_gemma":0.0003275331,"domain_scores_codex":[0.9960799,0.000293553,0.002369451,0.0005667598,0.0003420553,0.0003482574],"domain_scores_gemma":[0.996829,0.001108606,0.001208817,0.0002279632,0.0004005184,0.0002251305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004606996,0.0003717106,0.8167719,0.0001938557,0.0006053632,0.000001616671,0.0001982967,0.001279667,0.00002565685,0.000002304176,0.00008040628,0.1800086],"study_design_scores_gemma":[0.007470501,0.0001064593,0.8934633,0.0006179103,0.0003574955,0.00000447896,0.0000615046,0.09743468,0.00005229003,0.00001610503,0.0002324115,0.000182926],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948424,0.0003762429,0.0009692022,0.0008323869,0.0007757112,0.001158094,0.0009761357,0.00005462097,0.00001526154],"genre_scores_gemma":[0.9961182,0.0004815531,0.002577874,0.0004166907,0.00009274118,0.00008522921,0.0001724475,0.00003866668,0.00001663503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1798256,"threshold_uncertainty_score":0.9999983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03289625724407347,"score_gpt":0.3575459401471773,"score_spread":0.3246496829031039,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}