{"id":"W4404904568","doi":"10.2196/63109","title":"Diagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Concordance; Preprint; Clinical decision support system; Glaucoma; Computer science; Clinical decision making; Decision support system; Data science; Medicine; Artificial intelligence; Optometry; Family medicine; Ophthalmology","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":[0.03996005,0.0002554776,0.0005324333,0.003668068,0.0009551538,0.002311553,0.001049994,0.0007475131,0.002028682],"category_scores_gemma":[0.1546824,0.0002708143,0.0008802862,0.001648549,0.001469351,0.002147708,0.002150633,0.0007961505,0.0002191102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002068791,"about_ca_system_score_gemma":0.002303587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002025255,"about_ca_topic_score_gemma":0.003251796,"domain_scores_codex":[0.9712931,0.01910905,0.003161939,0.001403306,0.003694771,0.001337831],"domain_scores_gemma":[0.7750893,0.1952361,0.01125414,0.002819414,0.01318838,0.002412695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001748372,0.0006484933,0.6597824,0.001058452,0.0004636982,0.0005676037,0.2602659,0.0008897077,0.001693566,0.001124333,0.0009291994,0.0708283],"study_design_scores_gemma":[0.0001666421,0.002605963,0.7201335,0.0006833475,0.0002173621,0.0007326191,0.2626187,0.006415129,0.001566019,0.00263162,0.002114989,0.0001140176],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966691,0.0002380846,0.001868331,0.000119975,0.00001491711,0.0001560651,0.00005786983,0.000009347203,0.0008662324],"genre_scores_gemma":[0.9978429,0.00007476255,0.001728134,0.00003147746,0.000005717576,0.0001583554,0.00005498511,0.000003739589,0.00009994351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03996005,"threshold_uncertainty_score":0.2113314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0297020711444331,"score_gpt":0.4054785838180814,"score_spread":0.3757765126736483,"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."}}