{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001619619,0.0002356893,0.0002880251,0.001291602,0.002776818,0.001101191,0.0007139542,0.0005276019,0.00215962],"category_scores_gemma":[0.005122149,0.0004491728,0.0003848303,0.003669743,0.001059714,0.0004828244,0.0007779837,0.0006933702,0.0002419505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01667115,"about_ca_system_score_gemma":0.02301322,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9660455,"about_ca_topic_score_gemma":0.977747,"domain_scores_codex":[0.9976144,0.0002515432,0.0002587407,0.0002222611,0.001033681,0.0006193999],"domain_scores_gemma":[0.990867,0.0009866829,0.002399819,0.0001292402,0.003618284,0.001998955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003751137,0.00007357647,0.993284,0.00005577772,0.00001270257,0.00009120284,0.002442825,0.000027119,0.0001674132,0.00002073484,0.0007790112,0.003008202],"study_design_scores_gemma":[0.000004842199,0.00005927191,0.9932362,0.00002873762,0.000005966996,0.000058519,0.005781353,0.00008365417,0.00002797095,0.000004918026,0.0007020713,0.00000636707],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962112,0.000203751,0.00004383146,0.0005418098,0.000008330765,0.0001064506,0.0010133,0.000006177694,0.001865259],"genre_scores_gemma":[0.9981504,0.0002633701,0.0001696519,0.000439513,0.000005385661,0.00004032707,0.0003572833,0.000002641811,0.0005715017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0339545,"threshold_uncertainty_score":0.1209582,"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."}}