{"id":"W6901971717","doi":"10.6084/m9.figshare.12672670.v1","title":"Additional file 2 of Quantitative assessment of coronary plaque volume change related to triglyceride glucose index: The Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography IMaging (PARADIGM) registry","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Quantitative assessment; Logistic regression; Computed tomographic angiography; Triglyceride; Computed tomographic; Volume (thermodynamics); Multivariate statistics; Multivariate analysis; Angiography","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001615257,0.0008429206,0.001138473,0.001958964,0.0006583538,0.001363544,0.001639477,0.001036765,0.8242087],"category_scores_gemma":[0.03173895,0.0004766526,0.0006831605,0.003077928,0.0002170238,0.00139589,0.0007799333,0.0007853706,0.1154261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007440956,"about_ca_system_score_gemma":0.001256421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007426626,"about_ca_topic_score_gemma":0.01056013,"domain_scores_codex":[0.9991645,0.0001694059,0.0001847314,0.000197434,0.0001771384,0.0001068206],"domain_scores_gemma":[0.9764075,0.01704048,0.001941837,0.001369496,0.002713758,0.0005268431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0005239306,0.0001312329,0.006403898,0.001462205,0.0000573185,0.000104004,0.00005462416,0.0004012713,0.00007717492,0.0006311758,0.9803905,0.009762661],"study_design_scores_gemma":[0.009677843,0.0006055947,0.09325006,0.004459654,0.0004700971,0.001666383,0.0007994952,0.004841899,0.00138628,0.01639123,0.8661862,0.0002652372],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0003478234,0.00001411697,0.0002304028,0.00009668268,0.00001580326,0.00008617328,0.9980579,0.0001602006,0.0009908212],"genre_scores_gemma":[0.01932441,0.0001635384,0.003468159,0.0007166973,0.0001818144,0.002188122,0.958744,0.0007762086,0.01443712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8242087,"threshold_uncertainty_score":0.2507448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03095397849718813,"score_gpt":0.2935566509400379,"score_spread":0.2626026724428498,"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."}}