{"id":"W4393037410","doi":"10.1038/s41433-024-03026-z","title":"Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia","year":2024,"lang":"en","type":"review","venue":"Eye","topic":"Glaucoma and retinal disorders","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Health and Medical Research Council; Australian Government","keywords":"Glaucoma; Medicine; Optometry; Specialty; Health care; Neuro-ophthalmology; Family medicine; Ophthalmology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004235159,0.0002486848,0.0009527518,0.0001980652,0.00004997387,0.00001608586,0.0002792608,0.0001844151,0.000009200488],"category_scores_gemma":[0.0001682047,0.0001376615,0.0002724073,0.0006596489,0.0004098823,0.00002520261,0.00009935584,0.0004837554,0.000003228203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009914795,"about_ca_system_score_gemma":0.00019924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002508381,"about_ca_topic_score_gemma":0.00008524031,"domain_scores_codex":[0.9982139,0.0001612166,0.0008617302,0.0003556115,0.0001894086,0.0002180778],"domain_scores_gemma":[0.9985922,0.0006022307,0.000280924,0.0004179715,0.00007209532,0.00003456667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001148175,0.0001067432,0.008213593,0.1049025,0.00008134,0.000004237639,0.002090682,3.213656e-7,0.00005505063,0.001882903,0.00001418944,0.8825336],"study_design_scores_gemma":[0.002539769,0.001628175,0.2082601,0.3452809,0.01872601,0.0001367576,0.01763273,0.001322798,0.001375313,0.02261367,0.3782701,0.002213687],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.04180402,0.9537992,0.00006896364,0.0001248443,0.000078176,0.003900945,0.00006109793,0.00001339162,0.0001493709],"genre_scores_gemma":[0.07251682,0.9258159,0.0002906993,0.00001089265,0.00009008797,0.001062034,0.00004666726,0.00003860383,0.0001282841],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8803199,"threshold_uncertainty_score":0.5613674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02847162482499493,"score_gpt":0.3455938177386822,"score_spread":0.3171221929136873,"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."}}