{"id":"W4408538521","doi":"10.1016/j.jcjo.2025.02.013","title":"Correlation of point-wise retinal sensitivity with localized features of diabetic macular edema using deep learning","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Ophthalmology","topic":"Retinal Diseases and Treatments","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medizinische Universität Wien; Genentech; Universität Wien; Carl Zeiss Meditec AG; Apellis Pharmaceuticals","keywords":"Diabetic macular edema; Optical coherence tomography; Microperimetry; Ophthalmology; Retinal; Medicine; Macular edema; Nuclear medicine; Diabetic retinopathy; Diabetes mellitus; Endocrinology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001297127,0.000366736,0.0003481821,0.0006972344,0.00008838784,0.0004586182,0.0002811202,0.0003809882,0.0009468794],"category_scores_gemma":[0.004768142,0.000153817,0.0003296435,0.0003998337,0.0001788258,0.0004742318,0.0004822651,0.0003425146,0.0001591823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002051598,"about_ca_system_score_gemma":0.0001496392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000759941,"about_ca_topic_score_gemma":0.001176457,"domain_scores_codex":[0.9992414,0.0002882422,0.00006434572,0.0001902349,0.0001523934,0.00006345696],"domain_scores_gemma":[0.9970187,0.001520609,0.0008232396,0.0001883116,0.0002957031,0.0001535539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007540866,0.0001587033,0.9702248,0.00003297816,0.0003537489,0.00007382769,0.00003535558,0.002787106,0.002928445,0.0000357004,0.0001324623,0.02248277],"study_design_scores_gemma":[0.00003103119,0.0005160264,0.932215,0.00001761589,0.0001262191,0.0005991825,0.00007572812,0.06404593,0.001902524,0.0002413942,0.0002069241,0.00002255026],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970086,0.0002045601,0.002389105,0.00003412111,0.000003114337,0.000009072208,0.0001397523,0.00002077317,0.0001910138],"genre_scores_gemma":[0.9989994,0.00003010074,0.000764782,0.00001027597,0.000003326303,0.000005416192,0.0001085296,0.000002017824,0.00007618853],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001297127,"threshold_uncertainty_score":0.006859958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01154798056887214,"score_gpt":0.2755773629796162,"score_spread":0.2640293824107441,"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."}}