{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001196922,0.000114186,0.0002809394,0.0001745957,0.00009524625,0.00002695833,0.0001555269,0.0001049691,0.00001831397],"category_scores_gemma":[0.0005652053,0.00009289684,0.00005110068,0.0003065626,0.00003132964,0.0001117219,0.0002553432,0.0005534712,0.000007557966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005138249,"about_ca_system_score_gemma":0.00007592182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002065329,"about_ca_topic_score_gemma":0.00004837445,"domain_scores_codex":[0.9985184,0.0001710262,0.0004480729,0.0004197967,0.0002462276,0.0001964595],"domain_scores_gemma":[0.9993126,0.0001261873,0.00008502209,0.000380113,0.00004161268,0.00005442791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001678904,0.000123145,0.8646489,0.00012245,0.00001690263,0.00001373941,0.0001108215,0.000006579349,0.00008528428,0.00001015112,0.0002721946,0.134422],"study_design_scores_gemma":[0.001930067,0.0002128914,0.741921,0.000443137,0.00004417095,0.00000721457,0.000214278,0.2511418,0.00001306491,0.00003098278,0.003972823,0.00006850983],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.962133,0.0006573967,0.03242901,0.001063757,0.0001755013,0.0003029337,0.000006230097,0.00007791361,0.003154216],"genre_scores_gemma":[0.9976683,0.0001484131,0.001230545,0.0002723493,0.00006318809,0.000004078102,0.0003745384,0.00001364301,0.000224956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2511352,"threshold_uncertainty_score":0.3788223,"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."}}