{"id":"W4283361787","doi":"10.1007/s10633-022-09879-7","title":"MERCI: a machine learning approach to identifying hydroxychloroquine retinopathy using mfERG","year":2022,"lang":"en","type":"article","venue":"Documenta Ophthalmologica","topic":"Drug-Induced Ocular Toxicity","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kensington Health; York University; University of Toronto","funders":"Mitacs","keywords":"Hydroxychloroquine; Medicine; Retinopathy; Retinal; Ophthalmology; Clinical trial; Optometry; Artificial intelligence; Disease; Internal medicine; Computer science; Coronavirus disease 2019 (COVID-19)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001167001,0.0004179249,0.0006751081,0.000377607,0.0009093569,0.00008871594,0.0004873568,0.0001109062,0.002050279],"category_scores_gemma":[0.0002464214,0.0004001279,0.0002598582,0.0009937547,0.00007024182,0.0001814774,0.001287534,0.001133154,0.00009019174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007486906,"about_ca_system_score_gemma":0.00007076589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005229935,"about_ca_topic_score_gemma":7.628508e-7,"domain_scores_codex":[0.9961363,0.0004941176,0.0005752184,0.001006003,0.001000506,0.0007878212],"domain_scores_gemma":[0.9985819,0.00005831771,0.0002257407,0.0006822439,0.0000610546,0.000390708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"case_report","study_design_scores_codex":[0.001877037,0.004700532,0.1674425,0.0004403738,0.000689001,0.008959205,0.004159919,0.02179062,0.7842731,0.0007034101,0.001216194,0.003748119],"study_design_scores_gemma":[0.03524218,0.02302947,0.1729481,0.0009931966,0.003973986,0.2441666,0.02266567,0.1439209,0.1702948,0.003614126,0.1669315,0.01221939],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9741642,0.0003865025,0.000447006,0.0005829734,0.0003869299,0.001135986,0.00002232056,0.0002453807,0.02262863],"genre_scores_gemma":[0.9839761,0.000006207587,0.01114185,0.001279744,0.0001440711,0.0002079792,0.0002154523,0.00008777746,0.00294077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6139783,"threshold_uncertainty_score":0.9998451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07134996811524971,"score_gpt":0.3306422252678251,"score_spread":0.2592922571525753,"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."}}