{"id":"W4379347567","doi":"10.1017/cjn.2023.89","title":"C.3 Development and validation of a prediction model for perinatal arterial ischemic stroke in term neonates","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Blood Coagulation and Thrombosis Mechanisms","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary Laboratory Services; Workers Compensation Board of Alberta","funders":"","keywords":"Medicine; Logistic regression; Clinical prediction rule; Stroke (engine); Chorioamnionitis; Apgar score; Concordance; Pregnancy; Pediatrics; Birth weight; Gestational age; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.007534836,0.0009257724,0.0005838595,0.001851554,0.0005627681,0.001531115,0.001262856,0.0007941535,0.002653223],"category_scores_gemma":[0.01646761,0.0003514916,0.001408011,0.0006390032,0.0002526977,0.0003779383,0.0008135317,0.0009644281,0.00064932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001248092,"about_ca_system_score_gemma":0.003065203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0270825,"about_ca_topic_score_gemma":0.01027752,"domain_scores_codex":[0.9985506,0.0007406033,0.0001226433,0.0002264003,0.0002326806,0.0001270953],"domain_scores_gemma":[0.9932167,0.004288797,0.0004698538,0.0002105129,0.001624278,0.000189962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001140233,0.0008814947,0.7067258,0.000155826,0.0007931073,0.0005313921,0.0002612546,0.1712786,0.00137213,0.001869591,0.005829709,0.1091608],"study_design_scores_gemma":[0.00006311797,0.0002546662,0.03111104,0.00007133865,0.000156463,0.0001543729,0.00007697116,0.9658962,0.0006032326,0.0008955606,0.0006963557,0.00002070207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8697363,0.0004260455,0.1192548,0.001280556,0.0001536345,0.0006961034,0.002961494,0.001038429,0.004452562],"genre_scores_gemma":[0.9602497,0.0001099106,0.03629522,0.0000936761,0.00002755294,0.0003964006,0.001791236,0.00002858627,0.001007671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0270825,"threshold_uncertainty_score":0.05384976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05299863233469583,"score_gpt":0.283211796571355,"score_spread":0.2302131642366592,"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."}}