{"id":"W3210468248","doi":"","title":"Machine Learning Based End-to-End Pipeline for Optical Coherence Tomography Angiography of Diabetic Retinopathy","year":2019,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Optical coherence tomography; Diabetic retinopathy; Medicine; Pipeline (software); Ophthalmology; Optical coherence tomography angiography; Coherence (philosophical gambling strategy); Radiology; Computer science; Diabetes mellitus; Physics; Endocrinology","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":[0.0006456231,0.0009847292,0.0009712,0.001222286,0.0006389962,0.001437402,0.001249878,0.001231748,0.008510321],"category_scores_gemma":[0.001384736,0.0004643502,0.0009311155,0.0007401814,0.0001684576,0.0007113917,0.001014655,0.001290295,0.004894346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005952334,"about_ca_system_score_gemma":0.001639168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007149525,"about_ca_topic_score_gemma":0.01428178,"domain_scores_codex":[0.9996513,0.00004438371,0.00002329623,0.00008311618,0.0001014909,0.00009645583],"domain_scores_gemma":[0.9994889,0.0001594938,0.00003433404,0.00006042051,0.0002040426,0.00005266232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009855687,0.0004961146,0.00593891,0.0001805166,0.0001475333,0.0003576363,0.00007329782,0.02126451,0.03749574,0.001404573,0.01999114,0.9116644],"study_design_scores_gemma":[0.00005642494,0.0002307125,0.005197718,0.00003227471,0.00006699081,0.0004587768,0.00005080369,0.9404558,0.04319109,0.003657981,0.006564249,0.00003728351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05371717,0.001292518,0.9176031,0.0006876754,0.0001995087,0.0002988896,0.002060457,0.02086113,0.003279589],"genre_scores_gemma":[0.2752427,0.0006467518,0.7064736,0.0004545585,0.0001676116,0.0002918894,0.00466958,0.0004247229,0.01162872],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008510321,"threshold_uncertainty_score":0.02846986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02474022674682868,"score_gpt":0.3249686813638917,"score_spread":0.300228454617063,"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."}}