{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001364307,0.0002672114,0.0006422511,0.0009077984,0.0001842847,0.00003123565,0.0003724778,0.00009246952,0.0004149506],"category_scores_gemma":[0.001274705,0.0002222003,0.0003806444,0.003314222,0.00362407,0.0001321641,0.0001114107,0.0003293582,0.00005968419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003686312,"about_ca_system_score_gemma":0.0002824598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001128917,"about_ca_topic_score_gemma":6.449465e-7,"domain_scores_codex":[0.9973604,0.0001348424,0.0004622045,0.0008199954,0.000603451,0.000619086],"domain_scores_gemma":[0.9977931,0.0005119528,0.0002357539,0.0003344493,0.0005975524,0.0005272094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008705616,0.0001157225,0.5371593,0.00005310457,0.00002662928,0.00002127098,0.0001117281,0.0001014631,0.4620066,0.00003611485,0.00001280621,0.0002682644],"study_design_scores_gemma":[0.001141767,0.003984085,0.3709813,0.0003698805,0.0002809998,0.0001146534,0.0002016303,0.03187274,0.5898333,0.0007624276,0.00007171173,0.0003855418],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955383,0.0001922771,0.0002395248,0.0009502812,0.0001154971,0.0005847065,0.00001918238,0.00003904132,0.002321222],"genre_scores_gemma":[0.9872881,0.000001140227,0.01180012,0.0004311258,0.00003777439,0.00004957331,0.00003008978,0.00002100729,0.00034106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.166178,"threshold_uncertainty_score":0.9990875,"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."}}