{"id":"W2915465735","doi":"10.2147/opth.s193460","title":"&lt;p&gt;Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation&lt;/p&gt;","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004645467,0.0001904199,0.0005224299,0.00005699871,0.0001612026,0.00002139348,0.000061797,0.000443419,0.0001329407],"category_scores_gemma":[0.0004366998,0.0001503043,0.0001082128,0.00007992842,0.0002111923,0.00008548688,0.00007844284,0.0002787509,0.00003399515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002871567,"about_ca_system_score_gemma":0.00008833592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001002671,"about_ca_topic_score_gemma":3.826917e-7,"domain_scores_codex":[0.9981202,0.0001410393,0.0007379233,0.0005297785,0.0001292333,0.000341812],"domain_scores_gemma":[0.9977123,0.001509356,0.0001702968,0.0002484506,0.0001092714,0.000250314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004196499,0.006334686,0.7475229,0.003893482,0.0025421,0.006411789,0.001703219,0.02177345,0.007227148,0.02865649,0.04335506,0.1263832],"study_design_scores_gemma":[0.01484145,0.006383836,0.8467206,0.001457107,0.000941685,0.01003387,0.00006970141,0.09427171,0.0001535064,0.01773115,0.00665448,0.0007408559],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914681,0.002339527,0.001829333,0.0008212897,0.001213197,0.0009559304,0.00003539056,0.00005013321,0.001287114],"genre_scores_gemma":[0.9934183,0.0006692627,0.003751178,0.00017972,0.0003083719,0.0001205407,0.0003185809,0.00002689248,0.001207178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1256424,"threshold_uncertainty_score":0.6129231,"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."}}