{"id":"W4413927471","doi":"10.3390/vision9030077","title":"Predicting Pattern Standard Deviation in Glaucoma: A Machine Learning Approach Leveraging Clinical Data","year":2025,"lang":"en","type":"article","venue":"Vision","topic":"Glaucoma and retinal disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Clinical Research Institute; McGill University Health Centre; McGill University","funders":"","keywords":"Glaucoma; Standard deviation; Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology); Statistics; Ophthalmology; Mathematics; Medicine","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":[0.002100901,0.0006670503,0.0004343996,0.00188331,0.0001860973,0.0007456967,0.0003866842,0.0005063231,0.0005472043],"category_scores_gemma":[0.008854425,0.000177819,0.0004302621,0.0008697995,0.0002622176,0.0006331109,0.0004226869,0.0006688298,0.0002454692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003359395,"about_ca_system_score_gemma":0.0004451427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003206506,"about_ca_topic_score_gemma":0.004894536,"domain_scores_codex":[0.9992442,0.0002923372,0.0001066292,0.0001816056,0.000137716,0.00003759131],"domain_scores_gemma":[0.9956781,0.002716281,0.0006290556,0.000357658,0.0005314824,0.00008737467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003888139,0.0003135722,0.7165627,0.0001277585,0.0003237676,0.0003031261,0.0001035192,0.04547597,0.004994079,0.0002894141,0.001214731,0.2299025],"study_design_scores_gemma":[0.0000314609,0.0005089885,0.3109216,0.0001019526,0.0001364942,0.0006894211,0.0001313128,0.6784399,0.005462172,0.002427082,0.001096482,0.00005308727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.928969,0.001579352,0.06526712,0.0005590513,0.00007208109,0.0001070528,0.001531647,0.0003701339,0.001544565],"genre_scores_gemma":[0.9853945,0.0002445724,0.01328963,0.00005527819,0.00003625851,0.00002800147,0.0007371178,0.00001092066,0.0002036345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003206506,"threshold_uncertainty_score":0.01111078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03340765642121634,"score_gpt":0.3518266617802568,"score_spread":0.3184190053590405,"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."}}