{"id":"W4416103058","doi":"10.1016/j.bas.2025.105866","title":"Diagnostic performance and clinical applications of artificial intelligence for intracranial bleeding detection: A meta-analysis","year":2025,"lang":"en","type":"article","venue":"Brain and Spine","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"","keywords":"Workflow; Diagnostic accuracy; Applications of artificial intelligence; Artificial neural network; MEDLINE","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0006311865,0.00008072295,0.0005676861,0.0002068595,0.0001089899,0.00001636417,0.00004375165,0.00007382482,0.00004506524],"category_scores_gemma":[0.0006529472,0.00005925374,0.0002649749,0.0005633652,0.0001441636,0.00003423032,0.00003343825,0.0001370222,0.000001357802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006407502,"about_ca_system_score_gemma":0.00003613421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001053008,"about_ca_topic_score_gemma":0.00002701224,"domain_scores_codex":[0.9991376,0.00002475649,0.0004117062,0.0002116708,0.00008824843,0.0001260408],"domain_scores_gemma":[0.998989,0.000662291,0.00004957346,0.0001253698,0.000107115,0.0000666669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007297721,0.0001000318,0.0006989726,0.0005634061,0.004776047,0.00000174419,0.00003048579,0.00000381805,0.0001719344,0.002791801,0.000009091938,0.9907797],"study_design_scores_gemma":[0.0007512453,0.001143412,0.01029604,0.00007122413,0.11784,0.00005589683,0.0003925193,0.8597265,0.003685653,0.003010932,0.002725995,0.0003005446],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.233958,0.02216793,0.7364179,0.005511865,0.00004718085,0.001624676,0.00001575433,0.00003543009,0.0002212875],"genre_scores_gemma":[0.9848517,0.004451945,0.009802654,0.0001379587,0.0001216305,0.0001906086,0.000006288407,0.000005223961,0.0004320123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9904792,"threshold_uncertainty_score":0.2416297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06812767466243459,"score_gpt":0.3887358727886888,"score_spread":0.3206081981262542,"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."}}