{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002851008,0.0007476172,0.008096304,0.0002723439,0.0001149985,0.00007222572,0.0004757624,0.000900548,0.001749842],"category_scores_gemma":[0.01106175,0.0004977822,0.001736654,0.001256622,0.0001008091,0.0001117413,0.0003755061,0.002617185,0.001527788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001630454,"about_ca_system_score_gemma":0.001623661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005218863,"about_ca_topic_score_gemma":0.000002958886,"domain_scores_codex":[0.9918151,0.0005844386,0.004768465,0.0003335543,0.001852121,0.0006462585],"domain_scores_gemma":[0.9945647,0.001697171,0.001007266,0.0009433923,0.0002295521,0.001557969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003128569,0.0000479934,7.160494e-7,0.5947962,0.0002056818,0.0001593294,0.00006856765,4.712673e-8,7.101634e-10,0.0000146637,0.002809724,0.401894],"study_design_scores_gemma":[0.0002030347,0.0001248749,8.378602e-8,0.4910737,0.001824443,0.0007253059,0.00004922115,0.0001637851,2.74946e-8,0.000001778616,0.5055611,0.0002726726],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[5.070402e-7,0.9900529,0.0006212148,0.000301222,0.000305766,0.006588053,0.00002136806,0.0002005383,0.001908377],"genre_scores_gemma":[4.487565e-7,0.9872886,0.0007766497,0.00836215,0.000219921,0.001799726,0.0006679172,0.0000760303,0.0008085422],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.5027514,"threshold_uncertainty_score":0.9997474,"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."}}