{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009183582,0.00009288725,0.0002829509,0.0003255414,0.0001140514,0.000006257834,0.0001004227,0.00001590853,0.0004042836],"category_scores_gemma":[0.00006808578,0.00008015663,0.0002118166,0.0003199333,0.00008835926,0.00007034566,0.00007786987,0.0003413014,0.000002349438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002434297,"about_ca_system_score_gemma":0.00005610431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006594119,"about_ca_topic_score_gemma":1.582133e-8,"domain_scores_codex":[0.9983507,0.0006893625,0.0004662494,0.0001104971,0.0002312397,0.0001519991],"domain_scores_gemma":[0.9991189,0.00005496687,0.0004712469,0.0001235856,0.0001393541,0.00009194847],"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":[0.007186807,0.001144645,0.3980442,0.0001071049,0.0003186835,0.03921119,0.001119746,0.0004548593,0.4724706,0.00001413908,0.0001751382,0.07975291],"study_design_scores_gemma":[0.003191438,0.01419414,0.7571359,0.0001589483,0.0002075699,0.2062346,0.0005843876,0.00006939819,0.01587397,0.0001100722,0.002014757,0.0002248339],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945312,0.0007716333,0.0001787713,0.00006884542,0.0004503432,0.0001179136,0.000002664128,0.000007365845,0.003871294],"genre_scores_gemma":[0.9993664,0.000004281363,0.0004243939,0.00002643921,0.00009854585,0.000001207733,0.000001056971,0.00002099454,0.00005666291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4565966,"threshold_uncertainty_score":0.4426622,"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."}}