{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001121594,0.00009956503,0.0004634415,0.002022642,0.00009190689,0.00004812123,0.0001329922,0.0001050033,0.00003018319],"category_scores_gemma":[0.00007091993,0.00007603478,0.0003289005,0.001660386,0.000101876,0.00008785194,0.000008165893,0.0004644044,0.000003464555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000681474,"about_ca_system_score_gemma":0.0007456028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001339401,"about_ca_topic_score_gemma":0.0009281143,"domain_scores_codex":[0.9987943,0.0002644033,0.0003881099,0.0001273676,0.0001846251,0.000241212],"domain_scores_gemma":[0.9992328,0.00008973759,0.00009148782,0.0001693857,0.0001992102,0.0002173217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005925041,0.000008763247,0.9561548,0.00006095285,0.002467738,0.002905174,0.0002522248,0.00843097,0.01994304,0.000608137,0.00616106,0.002947876],"study_design_scores_gemma":[0.0005751009,0.0003337348,0.9646512,0.0004563316,0.003931171,0.004467083,0.0001445753,0.01098273,0.00176072,0.0007792746,0.01168231,0.0002357238],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9141319,0.004001133,0.0783936,0.001440331,0.0004611885,0.000178233,0.000006885406,0.00002100654,0.001365658],"genre_scores_gemma":[0.9983864,0.0000312406,0.0008473747,0.0002142047,0.000495434,0.000001581075,0.000006666735,0.00001079828,0.000006229765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0842545,"threshold_uncertainty_score":0.3100609,"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."}}