{"id":"W4414510721","doi":"10.1038/s41598-025-17872-8","title":"A potential bioelectrical impedance equation for estimating skeletal muscle area using computed tomography in colorectal cancer","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canada Research Chairs","keywords":"Bioelectrical impedance analysis; Mean squared error; Bootstrapping (finance); Concordance correlation coefficient; Resampling; Correlation coefficient; Linear regression; Computed tomography; Regression analysis","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":[0.002631892,0.0005685439,0.0005906313,0.0008250264,0.0001599601,0.000672665,0.0007132275,0.0005239115,0.0008686102],"category_scores_gemma":[0.01267502,0.0002589989,0.0005719081,0.0007901241,0.0002008907,0.0007263747,0.0004124089,0.0006049472,0.0003760518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004284695,"about_ca_system_score_gemma":0.0005960509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002419502,"about_ca_topic_score_gemma":0.003392908,"domain_scores_codex":[0.9990405,0.0004748235,0.0001017166,0.0001695679,0.0001833917,0.00002988367],"domain_scores_gemma":[0.9979107,0.001219048,0.0002800808,0.0001463252,0.0004105884,0.00003336644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004762129,0.0002077163,0.7326199,0.0003221593,0.0004853007,0.000283616,0.0002572784,0.04061311,0.01059354,0.001838986,0.002711812,0.2095903],"study_design_scores_gemma":[0.00008524301,0.0005846094,0.3631703,0.000147055,0.0004562287,0.001357243,0.0001711822,0.6236697,0.004001643,0.002017815,0.004280456,0.00005859157],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4831893,0.003495466,0.5080237,0.001218646,0.000185962,0.0003438363,0.001151444,0.0008520319,0.00153965],"genre_scores_gemma":[0.8836354,0.0008280393,0.1131538,0.0001718717,0.00005284545,0.0003140253,0.0008733373,0.00004486497,0.0009259592],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002631892,"threshold_uncertainty_score":0.01391894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03206616793911364,"score_gpt":0.3232347919700225,"score_spread":0.2911686240309088,"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."}}