{"id":"W2959526451","doi":"10.1002/jcsm.12466","title":"Clinical and biological characterization of skeletal muscle tissue biopsies of surgical cancer patients","year":2019,"lang":"en","type":"article","venue":"Journal of Cachexia Sarcopenia and Muscle","topic":"Muscle Physiology and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Alberta","funders":"Consejo Nacional de Ciencia y Tecnología; Canadian Institutes of Health Research; Alberta Innovates - Technology Futures","keywords":"Medicine; Biopsy; Muscle biopsy; Cancer; Population; Cohort; Skeletal muscle; Pathology; Radiology; Surgery; Internal medicine","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":[0.004168095,0.0002311461,0.0004991067,0.00301633,0.0003404185,0.0009577373,0.0005621735,0.0005513573,0.001149665],"category_scores_gemma":[0.01168803,0.0002263975,0.0005161896,0.002315601,0.0005856242,0.0004724098,0.0005204885,0.000193763,0.0002361592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003378058,"about_ca_system_score_gemma":0.0003790241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007187599,"about_ca_topic_score_gemma":0.001630806,"domain_scores_codex":[0.9955897,0.001671249,0.001300715,0.0007407952,0.0005870213,0.0001104833],"domain_scores_gemma":[0.9886916,0.003716188,0.005450318,0.0004508944,0.001417379,0.000273534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005477608,0.00002862917,0.9753567,0.001816616,0.0006395761,0.000547283,0.0003556804,0.00006341103,0.00360557,0.0000499533,0.0003075936,0.01668129],"study_design_scores_gemma":[0.00004038121,0.0004701809,0.9882013,0.0007761219,0.0004485182,0.004665048,0.0007629566,0.0000762419,0.001639096,0.00006997104,0.002834321,0.00001573551],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9131706,0.08081679,0.001579553,0.0002480574,0.0000564512,0.0002099356,0.001984954,0.00001463674,0.001919094],"genre_scores_gemma":[0.9882118,0.007898748,0.001771393,0.0003276578,0.00006264423,0.0002481065,0.001204667,0.000006493777,0.0002685167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004168095,"threshold_uncertainty_score":0.02204323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01123660589382723,"score_gpt":0.2877164679437512,"score_spread":0.276479862049924,"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."}}