{"id":"W4408944727","doi":"10.2196/67178","title":"Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Toronto General Hospital; University of Toronto; York University; Trent University; McGill University","funders":"","keywords":"Artificial intelligence; Medicine; Machine learning; Logistic regression; Chronic pain; Physical therapy; Computer science; Physical medicine and rehabilitation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001880174,0.0008773542,0.0006576787,0.001609802,0.0003668572,0.001081411,0.0006296884,0.0006227766,0.00148932],"category_scores_gemma":[0.007788256,0.0002575149,0.0007112069,0.0009170408,0.0002414341,0.0004490018,0.0006094261,0.001117223,0.0004619364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001371365,"about_ca_system_score_gemma":0.001479795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03104558,"about_ca_topic_score_gemma":0.03707713,"domain_scores_codex":[0.9992645,0.0002461917,0.00006073759,0.0001700007,0.0001577744,0.0001008001],"domain_scores_gemma":[0.9967477,0.001993383,0.0004344125,0.0001184514,0.0005249208,0.000181064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007766642,0.001100409,0.8401245,0.0002069831,0.0002639449,0.0002609748,0.0003205782,0.04682045,0.0008817536,0.0001433901,0.003679062,0.1054213],"study_design_scores_gemma":[0.00005689251,0.0008210462,0.3277314,0.0001094132,0.0001667224,0.0001624807,0.0003997357,0.6682234,0.001015772,0.0004931334,0.0007752537,0.00004487514],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803218,0.0004849787,0.01348094,0.0009906555,0.00003895302,0.0003465494,0.002618067,0.0003006027,0.001417405],"genre_scores_gemma":[0.988613,0.0002047961,0.00867161,0.0000784178,0.00002745627,0.0001962704,0.001624682,0.000007508037,0.000576344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03104558,"threshold_uncertainty_score":0.06172979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009897883566211112,"score_gpt":0.3051625899551692,"score_spread":0.2952647063889581,"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."}}