{"id":"W4388211011","doi":"10.1016/j.ogla.2023.10.002","title":"Imaging Angle Recession Using Anterior Segment OCT","year":2023,"lang":"en","type":"article","venue":"Ophthalmology Glaucoma","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Medicine; Ophthalmology; Glaucoma; Optical coherence tomography; Optometry","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":[0.000466375,0.0005140053,0.0002540617,0.001819177,0.0002534155,0.001062651,0.0002152158,0.0006739917,0.003328717],"category_scores_gemma":[0.001096142,0.0003860932,0.0002466709,0.0004889214,0.0002189365,0.00125201,0.0004047679,0.0005918592,0.0006071661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002401372,"about_ca_system_score_gemma":0.0005261645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002075042,"about_ca_topic_score_gemma":0.004380643,"domain_scores_codex":[0.9997423,0.00006013938,0.00002902224,0.00002961148,0.00009420557,0.00004472795],"domain_scores_gemma":[0.9995338,0.000167267,0.00006524914,0.0000492571,0.0001388647,0.00004557475],"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":[0.001568982,0.0003940403,0.07492106,0.0005749853,0.0001459502,0.01176998,0.0002967099,0.00163971,0.4810159,0.001514131,0.004567227,0.4215913],"study_design_scores_gemma":[0.000351132,0.002429568,0.306103,0.00156212,0.0007634511,0.1486516,0.001647042,0.1154023,0.3602801,0.007570604,0.05497703,0.0002620724],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8067415,0.01055065,0.1386506,0.002071166,0.0002290318,0.0005934839,0.0009710055,0.001009946,0.03918254],"genre_scores_gemma":[0.8850428,0.005383466,0.1032563,0.0007614371,0.0002015596,0.0001386517,0.0002358646,0.0001887515,0.004791214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003328717,"threshold_uncertainty_score":0.01113564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02316275379655589,"score_gpt":0.2892756523889171,"score_spread":0.2661128985923611,"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."}}