{"id":"W4413049940","doi":"10.3233/shti250904","title":"Sharper Detection: Enhanced Techniques for Diabetic Retinopathy Grading","year":2025,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Oversampling; Blindness; Computer science; Grading (engineering); Diabetic retinopathy; Artificial intelligence; Machine learning; Medicine; Optometry; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002408307,0.001380491,0.000759126,0.002908799,0.0003723258,0.001443866,0.00139788,0.001429988,0.002408971],"category_scores_gemma":[0.005826861,0.0004160485,0.001064031,0.001040561,0.0004239801,0.00126407,0.001366284,0.002019313,0.001749528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006803206,"about_ca_system_score_gemma":0.000887443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003966716,"about_ca_topic_score_gemma":0.007546149,"domain_scores_codex":[0.9988714,0.0002439067,0.00006889625,0.0002910555,0.0004002093,0.0001244928],"domain_scores_gemma":[0.9984607,0.0005096304,0.0002337182,0.0002703368,0.0004286148,0.00009706877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004459191,0.0002419375,0.005734001,0.0003018765,0.000179368,0.0002508558,0.0001347059,0.03974999,0.04075817,0.003682643,0.01553155,0.892989],"study_design_scores_gemma":[0.00006275013,0.0004111031,0.00800395,0.0001750993,0.0002197149,0.001011699,0.0001000117,0.8816866,0.07675064,0.009244786,0.02223788,0.00009577592],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06771944,0.004966652,0.9081184,0.001471007,0.0005557824,0.0002322436,0.001072524,0.01023406,0.005629895],"genre_scores_gemma":[0.3533618,0.00225737,0.633434,0.001034646,0.0003462075,0.0001071379,0.001962707,0.0005490573,0.006947082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003966716,"threshold_uncertainty_score":0.0127365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0264809724125242,"score_gpt":0.3877326548529421,"score_spread":0.3612516824404179,"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."}}