{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002476544,0.0001021753,0.0003422115,0.00004012852,0.00002303237,0.000005078231,0.00009773481,0.000175427,0.00006488892],"category_scores_gemma":[0.0000442741,0.00007683116,0.00008930996,0.00004942239,0.0002417943,0.00001408122,0.00008694138,0.0001020127,8.049032e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002256949,"about_ca_system_score_gemma":0.0000496061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001053546,"about_ca_topic_score_gemma":0.000002857274,"domain_scores_codex":[0.9990429,0.0001098692,0.0004781907,0.0001610674,0.00009376862,0.0001142506],"domain_scores_gemma":[0.9992546,0.00003345343,0.0003766905,0.0001003324,0.0001590001,0.00007592066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003536541,0.0001916239,0.2121431,0.00004688364,0.00005504114,0.000001184874,0.0000494562,0.000002024926,0.7597263,0.00003125374,0.00005298657,0.0273465],"study_design_scores_gemma":[0.00202009,0.00150813,0.9603786,0.00003671063,0.00002851617,0.000006229795,0.00004142823,0.000007671696,0.02102741,0.00003641399,0.01480588,0.0001029153],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985844,0.0008962147,0.0000123271,0.00008969331,0.0001737097,0.0001113817,0.00002622211,0.000001259008,0.0001047193],"genre_scores_gemma":[0.9962765,0.003419488,0.00007584236,0.0000538082,0.00009662372,0.000001492039,0.00002761099,0.00000734729,0.00004131236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7482355,"threshold_uncertainty_score":0.3133084,"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."}}