{"id":"W2160499824","doi":"10.1210/jc.2009-0778","title":"Proteomic Profiling of Growth Hormone-Responsive Proteins in Human Peripheral Blood Leukocytes","year":2009,"lang":"en","type":"article","venue":"The Journal of Clinical Endocrinology & Metabolism","topic":"Growth Hormone and Insulin-like Growth Factors","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"World Anti-Doping Agency; University of New South Wales","keywords":"Peripheral blood; Profiling (computer programming); Human growth hormone; Proteomics; Growth hormone; Hormone; Biology; Immunology; Computational biology; Medicine; Biochemistry; Computer science; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002151063,0.0002349563,0.0002494213,0.0003868524,0.0001450739,0.0002634289,0.0001043109,0.0002343335,0.0006801623],"category_scores_gemma":[0.0002232735,0.00007952969,0.0001398039,0.000321069,0.0001262883,0.0001004993,0.0001300443,0.0001788602,0.000251034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007382181,"about_ca_system_score_gemma":0.0001235543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002231579,"about_ca_topic_score_gemma":0.000276805,"domain_scores_codex":[0.9998896,0.00002534864,0.00001032328,0.00003375755,0.00002818231,0.00001272131],"domain_scores_gemma":[0.9999199,0.00001710263,0.00002275123,0.000005515236,0.00001962203,0.00001502108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001302116,0.00006201504,0.008390434,0.0001114169,0.00002045704,0.00009229584,0.00004236603,0.00004502638,0.9853668,0.000008010535,0.00004826448,0.004510765],"study_design_scores_gemma":[0.00032215,0.004710189,0.4118342,0.00006140641,0.000244039,0.002110661,0.0003752524,0.002201288,0.57493,0.0001390074,0.003052824,0.00001894217],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957361,0.001897084,0.00144404,0.00004845707,0.00001400344,0.00005712869,0.0005116099,0.00001821666,0.0002734024],"genre_scores_gemma":[0.9901575,0.001713831,0.005598048,0.0001245902,0.00003759174,0.0001534354,0.001250301,0.000008147714,0.0009565575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006801623,"threshold_uncertainty_score":0.002275348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03687286953986556,"score_gpt":0.3513240860174358,"score_spread":0.3144512164775702,"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."}}