{"id":"W2915465735","doi":"10.2147/opth.s193460","title":"<p>Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation</p>","year":2019,"lang":"en","type":"article","venue":"Clinical ophthalmology","topic":"Vasculitis and related conditions","field":"Medicine","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Toronto Metropolitan University; McMaster University; University of Manitoba; University of British Columbia; McGill University; University of Ottawa; Université Laval; University of Saskatchewan; University of Toronto; MacEwan University; Western University; Université de Sherbrooke; Queen's University","funders":"","keywords":"Medicine; Giant cell arteritis; Logistic regression; Regression; Artificial neural network; Arteritis; Ophthalmology; Internal medicine; Artificial intelligence; Cardiology; Statistics; Disease; Vasculitis; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.02231089,0.001081973,0.0007723442,0.001651043,0.0003034119,0.001310627,0.001117522,0.000899647,0.000932852],"category_scores_gemma":[0.05787868,0.0004541862,0.0009038537,0.0008371518,0.0005052208,0.001122696,0.001137964,0.001329516,0.0004396256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168871,"about_ca_system_score_gemma":0.001813976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009478712,"about_ca_topic_score_gemma":0.00520546,"domain_scores_codex":[0.9928276,0.00533534,0.0003601631,0.0005833833,0.0006760845,0.0002175162],"domain_scores_gemma":[0.9693769,0.02451147,0.001358961,0.001020768,0.003342472,0.0003893923],"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":[0.002756168,0.0009838461,0.6550855,0.0003684882,0.00151523,0.0003356221,0.0002895493,0.1498179,0.001107691,0.0007937026,0.003051824,0.1838945],"study_design_scores_gemma":[0.0001689185,0.0006992397,0.03611057,0.00009454485,0.0002176321,0.0001517165,0.0000963549,0.9604812,0.0008580716,0.0007140287,0.0003808305,0.00002688065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9153171,0.001257921,0.07873624,0.0009817852,0.0001695148,0.0005008595,0.0007692449,0.000456706,0.001810635],"genre_scores_gemma":[0.9600489,0.0002610373,0.03798055,0.0001303491,0.00005240204,0.0002718988,0.0008501874,0.00003599685,0.0003686717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02231089,"threshold_uncertainty_score":0.1179926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06237822646981016,"score_gpt":0.3367594677683619,"score_spread":0.2743812412985517,"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."}}