{"id":"W4386826466","doi":"10.1016/j.media.2023.102938","title":"GAMMA challenge: Glaucoma grAding from Multi-Modality imAges","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":102,"is_retracted":false,"has_abstract":false,"ca_institutions":"DiagnoCure (Canada); École de Technologie Supérieure","funders":"","keywords":"Glaucoma; Optical coherence tomography; Fundus photography; Grading (engineering); Medicine; Optometry; Fundus (uterus); Ophthalmology; Optic disc; Modality (human–computer interaction); Modalities; Artificial intelligence; Blindness; Computer science; Retinal; Fluorescein angiography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003174691,0.001264019,0.0008078176,0.003246258,0.0005024658,0.001768847,0.001287115,0.002402495,0.005414748],"category_scores_gemma":[0.009039041,0.0004079459,0.0007873163,0.0008209297,0.0002705906,0.001142006,0.002313062,0.0009399376,0.003010705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004528735,"about_ca_system_score_gemma":0.0009537662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005398747,"about_ca_topic_score_gemma":0.01101232,"domain_scores_codex":[0.9989344,0.0003024309,0.000112465,0.0001803921,0.0003735365,0.00009678568],"domain_scores_gemma":[0.997728,0.000848021,0.0001362875,0.0002530268,0.0006033543,0.0004312818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002443724,0.0006555456,0.02450483,0.001314219,0.0006032871,0.001742047,0.0002375912,0.008147406,0.03682342,0.001324971,0.1314153,0.7907877],"study_design_scores_gemma":[0.001735818,0.002988376,0.2288093,0.001790555,0.001265801,0.02503371,0.001594927,0.4453054,0.1039342,0.01932229,0.1674625,0.0007570338],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5443184,0.01734962,0.3241304,0.008217691,0.002192489,0.004625829,0.04707843,0.02921146,0.02287562],"genre_scores_gemma":[0.572558,0.00490578,0.3696049,0.001894694,0.00107237,0.001555778,0.03088095,0.002861697,0.01466588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005414748,"threshold_uncertainty_score":0.01811415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02778191829118971,"score_gpt":0.3494678826587598,"score_spread":0.3216859643675701,"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."}}