{"id":"W3003190198","doi":"10.1038/s41746-020-0219-5","title":"Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database","year":2020,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Meningioma and schwannoma management","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Children's Hospital; Montreal Neurological Institute and Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Fondation des Etoiles; Canada First Research Excellence Fund; Canada Research Chairs; National Institutes of Health; U.S. Department of Health and Human Services; Government of Canada; McGill University","keywords":"Generalizability theory; Logistic regression; Surveillance, Epidemiology, and End Results; Meningioma; Medicine; Malignancy; Random forest; Epidemiology; Machine learning; Artificial intelligence; Computer science; Radiology; Internal medicine; Cancer registry; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00250755,0.0003007293,0.0004182136,0.001322671,0.0001278978,0.0005799811,0.0003290297,0.0003271224,0.0009088507],"category_scores_gemma":[0.01163755,0.0001448943,0.000469929,0.001075763,0.00007805876,0.0004376167,0.000449478,0.0004631637,0.0004605106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004033777,"about_ca_system_score_gemma":0.0005994647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008978009,"about_ca_topic_score_gemma":0.01109568,"domain_scores_codex":[0.9991223,0.00034274,0.0001364133,0.0002039352,0.0001453018,0.00004935741],"domain_scores_gemma":[0.9959187,0.00231483,0.0007045383,0.0005016442,0.0003795528,0.0001808537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004526566,0.0001761034,0.9172122,0.00008967281,0.0002866773,0.0002262535,0.00008580992,0.02046773,0.0003460822,0.0005194185,0.009028947,0.05110834],"study_design_scores_gemma":[0.0001517604,0.0004015318,0.5310636,0.0001183567,0.0003400107,0.000648444,0.0002744737,0.4508213,0.002319019,0.002442583,0.01133932,0.00007965177],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9457644,0.0004312935,0.01096257,0.001013163,0.00003806981,0.0001342073,0.03920625,0.000592343,0.001857654],"genre_scores_gemma":[0.958011,0.0002839925,0.01121511,0.00009694829,0.00003172215,0.0001049814,0.02986335,0.00002985732,0.0003630532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008978009,"threshold_uncertainty_score":0.01785153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1084484214103717,"score_gpt":0.3049089641371439,"score_spread":0.1964605427267722,"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."}}