{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001380614,0.0001583658,0.0001757197,0.0002092114,0.0001205576,0.00002850973,0.000209696,0.00008273694,0.0003017749],"category_scores_gemma":[0.00001401125,0.0001640994,0.00007015275,0.0007774497,0.00008035327,0.0001145736,0.00008263662,0.0001610014,0.0003771348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005323558,"about_ca_system_score_gemma":0.00001113157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001473963,"about_ca_topic_score_gemma":5.475384e-7,"domain_scores_codex":[0.9989958,0.0000330568,0.0002198164,0.0002394453,0.0001072956,0.0004045689],"domain_scores_gemma":[0.9994654,0.00006295086,0.00002748438,0.0003241767,0.00002668044,0.00009332485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002078725,0.0001006324,0.6418684,0.0001341964,0.00008897996,0.0009767552,0.0002368861,0.0008698825,0.3394982,0.0004893881,0.002493037,0.01322295],"study_design_scores_gemma":[0.0006192214,0.00008882867,0.6980789,0.000169229,0.00005790303,0.002222058,0.0002678757,0.2790453,0.01367302,0.003345873,0.001619205,0.000812595],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904894,0.000252775,0.0009394052,0.0001162108,0.0003652596,0.0001838658,0.00001144231,0.0006840466,0.006957543],"genre_scores_gemma":[0.9962234,0.00001811171,0.003459933,0.00001784614,0.00006350193,0.00006282015,0.00001684198,0.000040184,0.00009734924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3258252,"threshold_uncertainty_score":0.6691781,"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."}}