{"id":"W4410777321","doi":"10.2196/71374","title":"Development of a Cohesive Predictive Model for Substance Use Disorder Rehabilitation Using Passive Digital Biomarkers, Psychological Assessments, and Automated Facial Emotion Recognition: Protocol for a Prospective Cohort Study","year":2025,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Rehabilitation; Clinical psychology; Psychology; Emotion recognition; Artificial intelligence; Applied psychology; Computer science; Physical medicine and rehabilitation; Medicine; Physical therapy; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03285392,0.002067169,0.001484812,0.001054227,0.001351564,0.0009765122,0.00234942,0.001316173,0.01173337],"category_scores_gemma":[0.03897278,0.001231128,0.004253593,0.0007842145,0.0006780454,0.0005312455,0.00192672,0.002065889,0.001895799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001169999,"about_ca_system_score_gemma":0.004971283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006305658,"about_ca_topic_score_gemma":0.004800444,"domain_scores_codex":[0.9929742,0.00464245,0.0006931167,0.0009047328,0.0005616596,0.0002238662],"domain_scores_gemma":[0.9859601,0.007717633,0.0007779687,0.002626712,0.002582341,0.000335139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.04307932,0.01440497,0.2119762,0.004191015,0.005544785,0.001742624,0.002125496,0.1857306,0.01095481,0.01107201,0.02592537,0.4832528],"study_design_scores_gemma":[0.02370695,0.03634941,0.1620324,0.001718662,0.005338192,0.001160131,0.001866708,0.6868457,0.0169115,0.01774394,0.04557592,0.0007504917],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"protocol","genre_scores_codex":[0.1375265,0.0004371091,0.5902336,0.0005659933,0.0003533146,0.2475147,0.02019347,0.0015005,0.001674791],"genre_scores_gemma":[0.1553955,0.0002306187,0.2816195,0.0001344584,0.00004881768,0.5514156,0.009819985,0.0001067191,0.001228843],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.03285392,"threshold_uncertainty_score":0.1737502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2969792008486836,"score_gpt":0.577243984343511,"score_spread":0.2802647834948274,"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."}}