{"id":"W4232417867","doi":"10.32920/14640006.v1","title":"Comprehensive data visualization for high resolution endovascular carotid arterial wall imaging","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; Colibri Technologies (Canada); St. Michael's Hospital; Toronto Metropolitan University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Optical coherence tomography; Medicine; Stent; Thrombus; Radiology; Carotid arteries; Apposition; Angioplasty; High resolution; Biomedical engineering; Surgery; Internal medicine; Geology; Remote sensing","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":[0.0009445493,0.0004409138,0.0002542369,0.00101574,0.0002384223,0.0008813047,0.0003184109,0.000557715,0.003436577],"category_scores_gemma":[0.002166196,0.0002962626,0.0002791449,0.000514239,0.0002725004,0.000889281,0.0007204768,0.0004402531,0.0007301536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001670484,"about_ca_system_score_gemma":0.0003083922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003126951,"about_ca_topic_score_gemma":0.0004977408,"domain_scores_codex":[0.9996055,0.0001501138,0.00003399163,0.00004494708,0.0001300011,0.00003546902],"domain_scores_gemma":[0.9984944,0.0007066774,0.00009667093,0.0003324073,0.0003075643,0.00006234548],"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":[0.0004398388,0.0001211649,0.003198917,0.0003957117,0.0000471549,0.0005203015,0.0003235469,0.006108914,0.7547489,0.002948975,0.002418297,0.2287284],"study_design_scores_gemma":[0.00009792156,0.0008716708,0.02936787,0.0002038626,0.0001122772,0.004342414,0.0004651261,0.2009261,0.7131206,0.007309168,0.0430305,0.0001524257],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1129298,0.000964669,0.8784469,0.0003262643,0.00005356886,0.000205203,0.0008846423,0.003020701,0.003168304],"genre_scores_gemma":[0.3236372,0.0007705772,0.6729866,0.00009191147,0.00003467781,0.0002075963,0.0008586065,0.0004092013,0.001003574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003436577,"threshold_uncertainty_score":0.01149648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0387791161248219,"score_gpt":0.3066073590496952,"score_spread":0.2678282429248733,"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."}}