{"id":"W4212870357","doi":"10.2196/29806","title":"Early Prediction of Functional Outcomes After Acute Ischemic Stroke Using Unstructured Clinical Text: Retrospective Cohort Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Logistic regression; Receiver operating characteristic; Stroke (engine); Artificial intelligence; Machine learning; Gradient boosting; Population; Retrospective cohort study; Cohort; Test set; Physical therapy; Random forest; Computer science; Internal medicine","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":[0.002226103,0.000538259,0.0005859265,0.001287668,0.0006368594,0.0009950864,0.0007278371,0.00075196,0.001495904],"category_scores_gemma":[0.006343022,0.0006062358,0.0009334018,0.001408113,0.0004567008,0.001159263,0.0008387076,0.0009875082,0.0005220496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004181161,"about_ca_system_score_gemma":0.0005252323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00571398,"about_ca_topic_score_gemma":0.005682366,"domain_scores_codex":[0.9987521,0.0002748791,0.0001895781,0.0004300992,0.0002017638,0.0001515842],"domain_scores_gemma":[0.9960133,0.0008957299,0.001349786,0.0008065848,0.0005511746,0.0003835076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000314273,0.0001028367,0.9983801,0.00001247253,0.00009811426,0.0001240477,0.000128506,0.00004898895,0.0001065324,0.0000139212,0.0001008031,0.0005694497],"study_design_scores_gemma":[0.00004562061,0.0004029923,0.9976268,0.00001526636,0.0001151663,0.0003534752,0.0004038793,0.0006739676,0.00006279759,0.00005203443,0.0002330723,0.00001485053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990472,0.00006345589,0.0001195732,0.00001464901,0.00000493509,0.00002927193,0.0006036124,0.000002550496,0.0001147731],"genre_scores_gemma":[0.9983034,0.00007775935,0.0001524273,0.00002238092,0.00001166328,0.00003828915,0.001269792,0.000003719443,0.0001206101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00571398,"threshold_uncertainty_score":0.01177287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01864902316416124,"score_gpt":0.3136211832052265,"score_spread":0.2949721600410652,"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."}}