{"id":"W3134977123","doi":"10.31557/apjcp.2021.22.2.333","title":"Physical Features and Vital Signs Predict Serum Albumin and Globulin Concentrations Using Machine Learning","year":2021,"lang":"en","type":"article","venue":"Asian Pacific Journal of Cancer Prevention","topic":"Inflammatory Biomarkers in Disease Prognosis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Zhejiang Province","keywords":"Globulin; Body mass index; Albumin; Medicine; Blood pressure; Internal medicine; Serum albumin; Vital signs; Pulse pressure; Endocrinology; Surgery","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001980015,0.0001204761,0.0002499792,0.00006601265,0.000119428,0.00005611853,0.00003015582,0.00006380212,0.00006297151],"category_scores_gemma":[0.00008092083,0.0001100863,0.0001158757,0.0001277485,0.00009683202,0.0002167439,0.00002816698,0.000276238,3.947231e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008644558,"about_ca_system_score_gemma":0.0002741045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003718358,"about_ca_topic_score_gemma":0.0000140469,"domain_scores_codex":[0.9989931,0.000150574,0.0002801333,0.0001573238,0.0002652605,0.0001536062],"domain_scores_gemma":[0.9992861,0.00002799262,0.0002533767,0.00007678821,0.000184996,0.0001707906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000781842,0.0003888718,0.6725777,0.0004444257,0.001130139,0.0005492497,0.002157856,0.00005990621,0.2541823,0.0001711115,0.0005691483,0.06698751],"study_design_scores_gemma":[0.01290858,0.002011904,0.7524301,0.004968189,0.004974594,0.0082375,0.01117124,0.00727294,0.1883516,0.002416167,0.004399314,0.0008578732],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881913,0.00889487,0.0009409466,0.001040195,0.0002020114,0.0001770148,0.00002873435,0.00001377149,0.0005111246],"genre_scores_gemma":[0.9977018,0.0004466186,0.00122812,0.00002251934,0.0003354696,0.000003593046,0.0000131678,0.00001504871,0.0002336212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07985242,"threshold_uncertainty_score":0.4489191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01122596593468903,"score_gpt":0.2970225184796747,"score_spread":0.2857965525449856,"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."}}