{"id":"W4412792079","doi":"10.1001/jamanetworkopen.2025.24154","title":"AI-Driven Injury Reporting in Pediatric Emergency Departments","year":2025,"lang":"en","type":"article","venue":"JAMA Network Open","topic":"Injury Epidemiology and Prevention","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; SickKids Foundation; University of Toronto","funders":"Sickkids Research Institute; Hospital for Sick Children; York University","keywords":"Medicine; Medical record; Receiver operating characteristic; Emergency department; Medical emergency; Referral; Emergency medicine; Artificial intelligence; Family medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.003603845,0.0005588319,0.0003918312,0.001526629,0.0002500348,0.0009510135,0.001259469,0.0006219023,0.001398634],"category_scores_gemma":[0.01956961,0.0002734478,0.0006174442,0.001377387,0.0003571199,0.0009047467,0.0007013225,0.0008667083,0.0005503805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002136996,"about_ca_system_score_gemma":0.002260297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04952592,"about_ca_topic_score_gemma":0.03989476,"domain_scores_codex":[0.9978724,0.0006986826,0.0002186813,0.0006974473,0.0003947203,0.0001182052],"domain_scores_gemma":[0.9908347,0.005043797,0.001572563,0.0006782163,0.001651285,0.0002194285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008008552,0.0004636661,0.6339594,0.0005819796,0.0002549455,0.0005236028,0.0004040532,0.1938088,0.002383042,0.001181757,0.01287109,0.152767],"study_design_scores_gemma":[0.00007168239,0.0001761354,0.09750123,0.00006358853,0.00008826608,0.0003644459,0.0002625181,0.893086,0.002567949,0.002380311,0.003401013,0.00003686819],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8849912,0.001589829,0.08162002,0.002953899,0.0001406824,0.0005955779,0.02049887,0.003604933,0.004005158],"genre_scores_gemma":[0.9603447,0.0003429772,0.02596331,0.000308704,0.00007279758,0.0001231102,0.01208295,0.00003814115,0.0007232953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04952592,"threshold_uncertainty_score":0.09847528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03354040616874382,"score_gpt":0.4022367736778797,"score_spread":0.3686963675091359,"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."}}