{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009485009,0.0001901806,0.0005517565,0.00009480856,0.00008119402,0.00001354613,0.0000825163,0.00006045438,0.00001565261],"category_scores_gemma":[0.002270664,0.0001178028,0.00003608906,0.0002657404,0.0004587752,0.0001550835,0.000209982,0.0001513689,0.000001043401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001425651,"about_ca_system_score_gemma":0.00002650364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000478562,"about_ca_topic_score_gemma":0.00000127487,"domain_scores_codex":[0.9981584,0.0001164468,0.0007081357,0.0003986713,0.0003679116,0.0002504333],"domain_scores_gemma":[0.9985625,0.0005502502,0.0002723827,0.0002808944,0.00007560173,0.0002583404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004749238,0.000348781,0.8348512,0.001927909,0.001554212,0.001596844,0.01469078,0.00004546308,0.01785318,0.007845768,0.02318191,0.09135474],"study_design_scores_gemma":[0.0168713,0.01020301,0.9185583,0.002102052,0.0009946938,0.001765014,0.007666209,0.01148128,0.0009708379,0.00069209,0.02806258,0.0006326488],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820166,0.001613441,0.000552458,0.007301269,0.0001942017,0.0004971084,0.0004411848,0.00004408238,0.007339691],"genre_scores_gemma":[0.9985068,0.00006609119,0.000453662,0.0003385812,0.0002395636,0.0000064183,0.0003373023,0.00001412259,0.00003745612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09072208,"threshold_uncertainty_score":0.4803858,"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."}}