{"id":"W1513225749","doi":"10.1155/2015/623619","title":"Combined Methods for Diabetic Retinopathy Screening, Using Retina Photographs and Tear Fluid Proteomics Biomarkers","year":2015,"lang":"en","type":"article","venue":"Journal of Diabetes Research","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"European Social Fund; UCL Institute of Ophthalmology, University College London; Peterborough K. M. Hunter Charitable Foundation; Moorfields Eye Hospital NHS Foundation Trust; Hungarian Scientific Research Fund; Nemzeti Kutatási és Technológiai Hivatal; European Commission; Biomedical Research Council; Canadian Institutes of Health Research; National Institute for Health and Care Research; Ontario Ministry of Health and Long-Term Care","keywords":"Diabetic retinopathy; Medicine; Ophthalmology; Retina; Proteomics; Diabetes mellitus; Chemistry; Biology; Endocrinology; Neuroscience","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":[0.01290811,0.0001758961,0.0006758238,0.001081588,0.0001786962,0.0001367454,0.0002162918,0.0001122886,0.00001090777],"category_scores_gemma":[0.005422952,0.000135249,0.0002838834,0.0009519501,0.0004313884,0.0001548143,0.0001094263,0.0006464412,9.146908e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001016136,"about_ca_system_score_gemma":0.0002780819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002087885,"about_ca_topic_score_gemma":1.949927e-7,"domain_scores_codex":[0.9968473,0.0008226663,0.0006312811,0.0002596709,0.0008127803,0.0006263001],"domain_scores_gemma":[0.9959136,0.0007386706,0.0002979949,0.0002775248,0.002122031,0.0006501996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001249287,0.0002533584,0.3691342,0.0004457002,0.0008543166,0.00003809049,0.0005132484,0.00001407064,0.5965745,0.00001208672,0.002744386,0.0281667],"study_design_scores_gemma":[0.02045108,0.0185491,0.02994329,0.004684408,0.00241337,0.0002955863,0.006106912,0.2843807,0.6178812,0.005748913,0.008511953,0.001033435],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988412,0.0022009,0.007025589,0.00147082,0.00009158223,0.000565798,0.000003942796,0.00001378493,0.0002156413],"genre_scores_gemma":[0.5950375,0.0001258766,0.404275,0.00007800803,0.0002467206,0.00001633454,0.000004366592,0.00004830409,0.0001679443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3972494,"threshold_uncertainty_score":0.6492172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1523129586203013,"score_gpt":0.4699793278367508,"score_spread":0.3176663692164496,"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."}}