{"id":"W2014944100","doi":"10.1016/j.mvr.2015.04.006","title":"In vivo optical imaging of human retinal capillary networks using speckle variance optical coherence tomography with quantitative clinico-histological correlation","year":2015,"lang":"en","type":"article","venue":"Microvascular Research","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Medical Research Council; National Health and Medical Research Council; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Retinal; Optical coherence tomography; Nerve fiber layer; Retina; Inner plexiform layer; Capillary action; Biomedical engineering; Speckle pattern; Anatomy; Materials science; Optics; Biology; Ophthalmology; Medicine; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004938641,0.0001482781,0.0001425616,0.0004287885,0.0002081652,0.0003307724,0.0001357312,0.0002939726,0.0004768435],"category_scores_gemma":[0.0006309301,0.0003131593,0.00008088283,0.0002904886,0.0003447053,0.0003940476,0.000209392,0.0002558766,0.0000700825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002527984,"about_ca_system_score_gemma":0.000408593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001907111,"about_ca_topic_score_gemma":0.003454727,"domain_scores_codex":[0.9998569,0.00005753594,0.000005833562,0.00002431952,0.00003942124,0.00001592041],"domain_scores_gemma":[0.9996738,0.0001748826,0.00004333107,0.00003366321,0.00005565964,0.00001864222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004067193,0.0001046655,0.005331644,0.00008419559,0.00001619627,0.000117692,0.0001294432,0.00220533,0.9768147,0.0009327244,0.0001579821,0.0136987],"study_design_scores_gemma":[0.00009571002,0.0006332586,0.07595663,0.00002484825,0.00008756208,0.001879679,0.0002112889,0.1747966,0.7431207,0.001404619,0.001727912,0.00006131396],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9344109,0.0005627473,0.06355132,0.00006959066,0.000009740626,0.00004615559,0.000102586,0.0000950573,0.001152154],"genre_scores_gemma":[0.9697573,0.0003202404,0.02929128,0.00003385636,0.000007820532,0.00004299251,0.00004950257,0.00001594686,0.0004809859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001907111,"threshold_uncertainty_score":0.003792048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07451391070448324,"score_gpt":0.3531811626409562,"score_spread":0.278667251936473,"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."}}