{"id":"W3205127119","doi":"10.2196/27363","title":"Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review","year":2021,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Corneal surgery and disorders","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Moorfields Eye Charity; Moorfields Eye Hospital NHS Foundation Trust; University College London; Department of Health and Social Care; National Institute for Health and Care Research; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research","keywords":"Subclinical infection; Keratoconus; Algorithm; Machine learning; Cochrane Library; Artificial intelligence; Medicine; MEDLINE; Computer science; Cornea; Meta-analysis; Ophthalmology; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02033088,0.002019942,0.01123773,0.008995387,0.0005031351,0.002457669,0.00249456,0.002048385,0.003671394],"category_scores_gemma":[0.08759021,0.001014428,0.0123953,0.006687924,0.0009246493,0.00251499,0.001069762,0.001592064,0.0003106117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003281198,"about_ca_system_score_gemma":0.008674087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005270153,"about_ca_topic_score_gemma":0.01065314,"domain_scores_codex":[0.9844884,0.007714686,0.004511598,0.0008367866,0.002243059,0.0002054403],"domain_scores_gemma":[0.9157773,0.07149255,0.007457065,0.0007812202,0.004232744,0.0002592361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0003487176,0.00003181017,0.0009854469,0.9078866,0.0262295,0.00004721719,0.00006439537,0.0007725901,0.00006365826,0.0002505536,0.001347511,0.06197193],"study_design_scores_gemma":[0.001108979,0.0006184846,0.004389884,0.8007401,0.1763965,0.0003388334,0.0001330952,0.001730217,0.0003233698,0.001439412,0.01269103,0.0000901376],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000716972,0.9974375,0.0006129057,0.0002476557,0.0001066852,0.0004895294,0.0002070753,0.00001695987,0.0001647402],"genre_scores_gemma":[0.02675796,0.9659073,0.004795255,0.0006136129,0.0001813494,0.001326862,0.0002971514,0.00001121615,0.0001094307],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02033088,"threshold_uncertainty_score":0.1075212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05556084818745712,"score_gpt":0.3966984096967951,"score_spread":0.341137561509338,"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."}}