{"id":"W4407240598","doi":"10.3928/23258160-20241217-01","title":"A Cross-Sectional Survey of Optometrists in Canada Regarding Referral Patterns and a Needs Assessment for an Artificial Intelligence Referral Screening Tool for Epiretinal Membrane","year":2025,"lang":"en","type":"article","venue":"Ophthalmic surgery, lasers & imaging retina","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Referral; Medicine; Triage; Cross-sectional study; Optometry; Epiretinal membrane; Family medicine; Ophthalmology; Medical emergency; Visual acuity; Pathology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003838477,0.000348303,0.0009451928,0.001164226,0.0002106057,0.0001478413,0.0001871103,0.00008200169,0.00003378842],"category_scores_gemma":[0.002057214,0.0003563655,0.0002639654,0.001341428,0.0001584269,0.0002337065,0.00007604921,0.0003402886,1.798267e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005409228,"about_ca_system_score_gemma":0.001400175,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3514215,"about_ca_topic_score_gemma":0.02325222,"domain_scores_codex":[0.9966646,0.0002383734,0.001219954,0.00072062,0.0004917511,0.0006646513],"domain_scores_gemma":[0.9961255,0.00227115,0.0003785256,0.0003999803,0.000651847,0.0001729501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001367661,0.00009398603,0.9818094,0.0005078659,0.0001131019,0.00004311962,0.00003779339,0.0001780167,0.001359619,0.00003715517,0.000141026,0.01431122],"study_design_scores_gemma":[0.0005654422,0.0001074057,0.9527889,0.0007681574,0.0001062453,0.0001485908,0.0005919371,0.04024263,0.004228545,0.00005355534,0.00006572143,0.000332842],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885272,0.0001166876,0.009517047,0.0003895651,0.000375854,0.0005425923,0.0002204182,0.00003097625,0.00027963],"genre_scores_gemma":[0.9952835,0.00002537493,0.003546568,0.0001221055,0.00008753873,0.0001026497,0.000543198,0.00004405005,0.000244998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3281693,"threshold_uncertainty_score":0.9998888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08004150052272833,"score_gpt":0.3822077020744697,"score_spread":0.3021662015517413,"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."}}