{"id":"W4388249268","doi":"10.1016/j.nutos.2023.11.001","title":"Prevalence of and risk factors for pre-sarcopenia among healthcare professionals","year":2023,"lang":"en","type":"article","venue":"Clinical Nutrition Open Science","topic":"Nutrition and Health in Aging","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Cheng Kung University Hospital; National Cheng Kung University; National Science and Technology Council","keywords":"Sarcopenia; Bioelectrical impedance analysis; Medicine; Logistic regression; Grip strength; Physical therapy; Gerontology; Malnutrition; Health care; Cohort; Internal medicine; Environmental health; Body mass index","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":[],"consensus_categories":[],"category_scores_codex":[0.0003666514,0.0002385165,0.0002427401,0.0006329882,0.0003806551,0.0004287942,0.0001704448,0.0004206586,0.002815602],"category_scores_gemma":[0.00136304,0.0002321769,0.0002336247,0.0004949987,0.0001594792,0.0002645338,0.00040992,0.0003062662,0.0002550145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001680716,"about_ca_system_score_gemma":0.000294664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004002315,"about_ca_topic_score_gemma":0.006221537,"domain_scores_codex":[0.9995259,0.00008400699,0.00005987556,0.00008504273,0.0001148083,0.0001302345],"domain_scores_gemma":[0.9992971,0.00008325012,0.0003468954,0.00002480761,0.0001045273,0.000143434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002641476,0.00003503826,0.9983025,0.00001302976,0.00001218634,0.00007401099,0.00007607855,0.000004626473,0.00009985497,0.00000529728,0.00007633153,0.001274789],"study_design_scores_gemma":[0.000003169785,0.00006260748,0.9992464,0.0000168255,0.000007744847,0.0002277394,0.0002607879,0.00004844289,0.00002158603,0.000007502942,0.00009556157,0.000001750518],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987996,0.0005194277,0.00005036524,0.00007021622,0.00000774289,0.00001108604,0.0001078841,0.000002921143,0.0004307897],"genre_scores_gemma":[0.9992823,0.0002325023,0.000053843,0.00003602211,0.00001245157,0.0000106043,0.0001350997,5.794321e-7,0.0002366218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004002315,"threshold_uncertainty_score":0.009419143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2149464698933581,"score_gpt":0.5559740359516798,"score_spread":0.3410275660583217,"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."}}