{"id":"W4392429723","doi":"10.3390/bioengineering11030250","title":"Using Fused Data from Perimetry and Optical Coherence Tomography to Improve the Detection of Visual Field Progression in Glaucoma","year":2024,"lang":"en","type":"article","venue":"Bioengineering","topic":"Glaucoma and retinal disorders","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Glaucoma; Optical coherence tomography; Visual field; Bayesian probability; Linear regression; Medicine; Artificial intelligence; Ophthalmology; Computer science; Pattern recognition (psychology); Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008562679,0.00007600357,0.0001093162,0.0001121549,0.00001620317,0.00002047511,0.00006794989,0.00005510552,0.000005631043],"category_scores_gemma":[0.0000631821,0.00005085752,0.00002221233,0.0002787171,0.00002345245,0.00004816323,0.00009006081,0.0001348045,8.909856e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001006663,"about_ca_system_score_gemma":0.00001450515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001523813,"about_ca_topic_score_gemma":0.00001607622,"domain_scores_codex":[0.9994732,0.000007704914,0.0001236106,0.0001835922,0.000103162,0.0001088025],"domain_scores_gemma":[0.9996927,0.00007335341,0.0000099907,0.0001662447,0.00001053183,0.00004715244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007454448,0.00002733177,0.03134463,0.0001509171,0.0000219722,0.00001671241,0.0001183307,0.00000169746,0.9401261,0.00001121606,0.000005119733,0.02810146],"study_design_scores_gemma":[0.0004882234,0.0007218061,0.3606751,0.001302652,0.0001007992,0.00002932571,0.0004837725,0.3301971,0.3054483,0.00001458459,0.0003617536,0.0001766863],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98598,0.002552257,0.01081507,0.0001674192,0.0001971617,0.000224605,0.000005149976,0.00003574081,0.000022591],"genre_scores_gemma":[0.9976151,0.00001436542,0.002270072,0.00001926698,0.00005871665,0.000004771854,0.000005301024,0.00001010879,0.000002311421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6346778,"threshold_uncertainty_score":0.2073909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01971444412326832,"score_gpt":0.3104016786002055,"score_spread":0.2906872344769371,"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."}}