{"id":"W2615000063","doi":"10.1017/cem.2017.301","title":"P099: Age related rates of abnormal CT findings in otherwise low risk minor head injury patients over 65","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Interior Health","funders":"","keywords":"Medicine; Pathological; Risk factor; Retrospective cohort study; Gold standard (test); Age adjustment; Computed tomography; Head injury; Age groups; Pediatrics; Radiology; Internal medicine; Surgery; Epidemiology; Demography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002675053,0.0002657905,0.0002385867,0.000983446,0.0003423721,0.0006095989,0.0003366752,0.0006387566,0.008911712],"category_scores_gemma":[0.002997394,0.0002249963,0.0005289365,0.001131851,0.0002512881,0.0008748986,0.0005522949,0.0008755161,0.001065638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002138565,"about_ca_system_score_gemma":0.0003372393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008718329,"about_ca_topic_score_gemma":0.009273466,"domain_scores_codex":[0.999765,0.00002358791,0.00003750291,0.00005627336,0.00004904689,0.0000685047],"domain_scores_gemma":[0.9979125,0.0003206062,0.001116986,0.0000690443,0.0002129747,0.0003678928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001335888,0.00001462503,0.9980888,0.000007740989,0.00002485176,0.0001595776,0.00003224926,0.00002449703,0.0001063295,0.00001511993,0.0003311878,0.001061411],"study_design_scores_gemma":[0.000001987173,0.00002901013,0.9990182,0.000005439671,0.00001727992,0.0004976309,0.0001513993,0.00007116207,0.00002570662,0.0000265425,0.000152097,0.000003574364],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948294,0.0002828159,0.00008772406,0.0001867537,0.00003257914,0.000009232356,0.002443243,0.00001198547,0.002116323],"genre_scores_gemma":[0.9985311,0.0001038359,0.00004267217,0.00005626091,0.00003258257,0.00000511367,0.0007950167,0.000004919157,0.0004284726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008911712,"threshold_uncertainty_score":0.02981257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02761177914370302,"score_gpt":0.3468976736136378,"score_spread":0.3192858944699348,"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."}}