{"id":"W4220884885","doi":"10.1177/11206721221087566","title":"KEDOP: Keratoconus early detection of progression using tomography images","year":2022,"lang":"en","type":"article","venue":"European Journal of Ophthalmology","topic":"Corneal surgery and disorders","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nortel (Canada)","funders":"Hyderabad Eye Research Foundation","keywords":"Keratoconus; Medicine; Visual acuity; Ophthalmology; Artificial intelligence; Convolutional neural network; Deep learning; Optometry; Cornea; Computer science","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":[0.0008208769,0.0004625359,0.0003922842,0.001982837,0.0002014387,0.0007582054,0.0005176969,0.0003789089,0.0009699654],"category_scores_gemma":[0.002360412,0.0002244472,0.000325309,0.0006712676,0.0001557131,0.0007775379,0.0005417851,0.0004294632,0.0003140614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004398706,"about_ca_system_score_gemma":0.0005461687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003160347,"about_ca_topic_score_gemma":0.007390876,"domain_scores_codex":[0.9996033,0.00009786661,0.00004337565,0.00008912369,0.0001301301,0.00003614464],"domain_scores_gemma":[0.9989964,0.0002137129,0.000309711,0.0000869999,0.0003054015,0.00008776415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001104824,0.0003561003,0.632279,0.0004940341,0.0002034932,0.0006706332,0.0001961504,0.005051155,0.03804282,0.0005341943,0.002455073,0.3186125],"study_design_scores_gemma":[0.00009663048,0.001010893,0.7365204,0.0002174088,0.0002113756,0.004516409,0.0003731427,0.2042261,0.04511136,0.0009904029,0.006625977,0.00009988143],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9178671,0.003605254,0.06975595,0.0004928173,0.0001118252,0.0004929468,0.002695617,0.001071158,0.003907299],"genre_scores_gemma":[0.9382063,0.0007814922,0.05771255,0.0000710771,0.0000534135,0.0001394826,0.001447415,0.00002788427,0.001560348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003160347,"threshold_uncertainty_score":0.006283939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0298185385102629,"score_gpt":0.2958828870399898,"score_spread":0.2660643485297269,"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."}}