{"id":"W2889149874","doi":"10.1016/j.jcjo.2018.05.006","title":"Support Vector Machines and logistic regression to predict temporal artery biopsy outcomes","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Ophthalmology","topic":"Vasculitis and related conditions","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Logistic regression; Support vector machine; Covariate; Artificial intelligence; Statistics; Regression; Pattern recognition (psychology); Computer science; Machine learning; Mathematics","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":[0.006443418,0.0008167066,0.00121256,0.001880923,0.0003522483,0.001470128,0.001068321,0.001145251,0.001752423],"category_scores_gemma":[0.02640216,0.0002929717,0.001128391,0.00124205,0.0003460945,0.001394864,0.0007274328,0.002641601,0.000714384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006677907,"about_ca_system_score_gemma":0.001331845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005311414,"about_ca_topic_score_gemma":0.004214889,"domain_scores_codex":[0.9977848,0.001164549,0.0002336821,0.0002843085,0.0003098148,0.0002227591],"domain_scores_gemma":[0.9822349,0.01392787,0.001131307,0.0005382531,0.001416606,0.0007510431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007186449,0.001374581,0.8220433,0.0001951584,0.001207725,0.0004081969,0.0002230346,0.01793477,0.0006887256,0.0007462209,0.006913695,0.1410782],"study_design_scores_gemma":[0.0004140887,0.001425564,0.2273883,0.0001500925,0.0008254173,0.00064969,0.0007991873,0.7609003,0.001029335,0.004995917,0.001331586,0.0000905685],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805585,0.002370264,0.01251109,0.001733061,0.0003854427,0.00006871556,0.001154607,0.0001566865,0.001061753],"genre_scores_gemma":[0.9934373,0.0005211104,0.00387867,0.0001010419,0.0002236376,0.00005494439,0.000984399,0.00002019047,0.0007787148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006443418,"threshold_uncertainty_score":0.03407639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03146420727563094,"score_gpt":0.3094468076455883,"score_spread":0.2779826003699574,"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."}}