{"id":"W4403226896","doi":"10.1016/j.cjca.2024.08.173","title":"DEBUNKING THE OBESITY PARADOX IN TAVR USING VOLUMETRIC BODY COMPOSITION ANALYSIS","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Medicine; Composition (language); Obesity; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003466435,0.0006269804,0.0008017696,0.001098015,0.0004713873,0.001797361,0.0008093971,0.0009024656,0.001033995],"category_scores_gemma":[0.01374782,0.0002979099,0.0004716738,0.0008537434,0.0005391345,0.001092814,0.001021997,0.002171673,0.0002043126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003184531,"about_ca_system_score_gemma":0.0006051083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004078303,"about_ca_topic_score_gemma":0.00636579,"domain_scores_codex":[0.9989039,0.0004099217,0.000103574,0.0001916967,0.0002874647,0.0001034102],"domain_scores_gemma":[0.9957576,0.001893874,0.00107828,0.0005524207,0.0004823722,0.0002353325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002671783,0.0005969177,0.869635,0.0002852799,0.001168772,0.000599971,0.0007516669,0.000871399,0.004218807,0.001820094,0.002285982,0.1150942],"study_design_scores_gemma":[0.0001088211,0.0008623508,0.9726721,0.0003731277,0.0008772108,0.001593255,0.0009199369,0.01072867,0.001763417,0.006348491,0.003677134,0.00007550899],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9817426,0.006138492,0.005423796,0.00192962,0.0004101254,0.00004393421,0.0002651549,0.00006885375,0.003977326],"genre_scores_gemma":[0.9955373,0.0009213006,0.002141194,0.0005915242,0.0002064645,0.00001930946,0.0001347609,0.00002407643,0.0004240721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004078303,"threshold_uncertainty_score":0.01833254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03205046270202672,"score_gpt":0.2898892784307888,"score_spread":0.257838815728762,"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."}}