{"id":"W4413255197","doi":"10.3389/fdata.2025.1605018","title":"Artificial intelligence for surgical outcome prediction in glaucoma: a systematic review","year":2025,"lang":"en","type":"review","venue":"Frontiers in Big Data","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Queen's University; University of Toronto; McMaster University","funders":"","keywords":"Glaucoma; Medicine; Generalizability theory; Scopus; MEDLINE; Machine learning; Artificial intelligence; Random forest; Evidence-based medicine; Systematic review; Intensive care medicine; Meta-analysis; Medical physics; Computer science; Statistics; Internal medicine; Ophthalmology; Pathology; Alternative 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00242143,0.0003569851,0.004793664,0.0007687487,0.00003035618,0.00003402698,0.0007871256,0.0002235272,0.000007303595],"category_scores_gemma":[0.003001192,0.000261151,0.0004720817,0.001352608,0.00005827414,0.00007682599,0.0002328055,0.0005271946,0.0000166612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002546159,"about_ca_system_score_gemma":0.0002954778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002825809,"about_ca_topic_score_gemma":0.00001235529,"domain_scores_codex":[0.9959069,0.0003500059,0.002373779,0.0007650823,0.0003003032,0.0003039361],"domain_scores_gemma":[0.9974124,0.0003321265,0.0003906832,0.001730896,0.00005447623,0.00007943884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000009462298,0.00006155494,0.0003736322,0.678459,0.0001227498,0.00006624972,0.000003170345,3.57971e-8,1.141159e-9,0.000009695945,0.008749562,0.3121449],"study_design_scores_gemma":[0.0000824469,0.00002014341,0.000001779895,0.7232624,0.006536045,0.00004961003,0.00002099536,0.001016537,2.881743e-8,0.00005576316,0.2687973,0.0001569613],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[1.601857e-7,0.9781142,0.01650303,0.0002113327,0.001031438,0.003296512,0.0007072998,0.00003150959,0.000104464],"genre_scores_gemma":[0.000001121343,0.9912485,0.002233407,0.00007669729,0.0002305817,0.0004616169,0.004690801,0.00002501469,0.001032238],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.3119879,"threshold_uncertainty_score":0.9999841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1946163328448447,"score_gpt":0.4236188901402667,"score_spread":0.2290025572954219,"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."}}