{"id":"W4224885359","doi":"10.1016/j.xops.2022.100165","title":"Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia","year":2022,"lang":"en","type":"article","venue":"Ophthalmology Science","topic":"Retinopathy of Prematurity Studies","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"National Eye Institute; Research to Prevent Blindness; National Institutes of Health; Ulverscroft Foundation; National Science Foundation","keywords":"Receiver operating characteristic; Medicine; Fundus (uterus); Retinopathy of prematurity; Precision and recall; Population; Optometry; Artificial intelligence; Ophthalmology; Computer science; Internal medicine; Gestational age; Environmental health","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":[0.009192244,0.00009496266,0.0003509052,0.0002931924,0.0001995001,0.000006041166,0.0002360537,0.00004573476,0.0000131874],"category_scores_gemma":[0.001464075,0.00009393785,0.00003588902,0.0005456207,0.001020786,0.0001320116,0.000224228,0.0001604361,1.900959e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009504341,"about_ca_system_score_gemma":0.0002445211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009604634,"about_ca_topic_score_gemma":0.000006447452,"domain_scores_codex":[0.9977015,0.0002915899,0.0004394976,0.0004076844,0.0009239279,0.0002357624],"domain_scores_gemma":[0.9987468,0.0001532649,0.0002451433,0.0002832435,0.0005110347,0.00006049865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004460062,0.001951699,0.3268361,0.002715372,0.0001098256,0.0003348603,0.03320128,0.004749428,0.5152136,0.0145612,0.00001393205,0.09585262],"study_design_scores_gemma":[0.000956116,0.003854383,0.5233477,0.0003178311,0.0002604382,0.004172612,0.03065301,0.2747921,0.1518916,0.009482621,0.000002044875,0.0002695863],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977149,0.0002944627,0.0001360126,0.0001378141,0.0001964628,0.000931566,0.00001565584,0.00001024632,0.000562827],"genre_scores_gemma":[0.9910699,0.000001248858,0.008795626,0.000004309875,0.00001955677,0.00009639676,0.000002847836,0.000005606168,0.000004570885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.363322,"threshold_uncertainty_score":0.3830675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1078923906649102,"score_gpt":0.3835979997635655,"score_spread":0.2757056090986553,"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."}}