{"id":"W4399042218","doi":"10.3390/jcto2020005","title":"Artificial Intelligence in Glaucoma: A New Landscape of Diagnosis and Management","year":2024,"lang":"en","type":"article","venue":"Journal of Clinical & Translational Ophthalmology","topic":"Glaucoma and retinal disorders","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto","funders":"","keywords":"Glaucoma; Computer science; Geography; Data science; Artificial intelligence; Environmental resource management; Environmental science; Medicine; Ophthalmology","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":[0.0007626045,0.00009080932,0.0004806973,0.0002606143,0.00001025047,0.00000729296,0.00007323107,0.0001314865,0.0003671155],"category_scores_gemma":[0.0001271321,0.00006772839,0.0002395399,0.0002325511,0.0001243421,0.00006135671,0.00001227569,0.0003635635,0.000007411914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005421941,"about_ca_system_score_gemma":0.0001525958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000860494,"about_ca_topic_score_gemma":0.000004301162,"domain_scores_codex":[0.9980598,0.00008969341,0.001344177,0.0001471683,0.0002403619,0.0001187831],"domain_scores_gemma":[0.998706,0.0008915832,0.0001490243,0.00006314046,0.00006191876,0.0001283633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00143788,0.0005619862,0.8899186,0.0003016607,0.0002415468,0.00259796,0.0001576685,0.00001600065,0.00003168499,0.01181078,0.0003221082,0.09260216],"study_design_scores_gemma":[0.000502935,0.001172813,0.9604908,0.0004363909,0.0001861697,0.002457441,0.00007190857,0.0007799182,0.00002769549,0.03256424,0.001245196,0.00006447193],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9810284,0.004955806,0.001085325,0.01024196,0.0004153838,0.0001520822,0.000002477845,0.000003686155,0.002114894],"genre_scores_gemma":[0.9942811,0.001263412,0.004073601,0.00007490286,0.0002154218,0.000002161072,0.000002590027,0.00000840203,0.00007840245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09253769,"threshold_uncertainty_score":0.4019658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07720556313961441,"score_gpt":0.4143354427377317,"score_spread":0.3371298795981173,"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."}}