{"id":"W2095461923","doi":"10.1002/prca.200800190","title":"A proteomic approach combining MS and bioinformatic analysis for the detection and identification of biomarkers of administration of exogenous human growth hormone in humans","year":2009,"lang":"en","type":"article","venue":"PROTEOMICS - CLINICAL APPLICATIONS","topic":"Growth Hormone and Insulin-like Growth Factors","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Novo Nordisk; World Anti-Doping Agency","keywords":"Placebo; Peptide; Biomarker; Proteomics; Biomarker discovery; Glycoprotein; Internal medicine; Endocrinology; Quantitative proteomics; Recombinant DNA; Medicine; Human growth hormone; Growth hormone; Hormone; Chemistry; Chromatography; Biochemistry; Pathology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00123181,0.0001304716,0.0005133265,0.0003241011,0.0001167256,0.00001375312,0.000119451,0.0001538643,6.248669e-7],"category_scores_gemma":[0.000204483,0.0001057778,0.0001610908,0.0007498916,0.0003073596,0.0001047389,0.00002323391,0.000162992,1.93254e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002190753,"about_ca_system_score_gemma":0.00005856285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006428535,"about_ca_topic_score_gemma":0.00001101631,"domain_scores_codex":[0.9977683,0.00004745628,0.001636495,0.0002588388,0.0001578694,0.0001310811],"domain_scores_gemma":[0.998252,0.0001976931,0.0009125197,0.0003515311,0.0002195318,0.00006673746],"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":[0.001298791,0.00203688,0.09811004,0.0008665932,0.0009473705,1.921856e-7,0.001336878,0.00002015408,0.8755817,0.005761802,0.000005369907,0.01403424],"study_design_scores_gemma":[0.001791476,0.001288146,0.9330088,0.00002268067,0.0009464793,0.000004064118,0.0002548391,0.010803,0.04851303,0.003248476,0.000004060889,0.0001149154],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8419371,0.0001341467,0.1539471,0.0001762906,0.000005226293,0.003697068,0.00002898649,0.00001231346,0.00006183112],"genre_scores_gemma":[0.9898813,0.0002220893,0.009088267,0.00002215525,0.00001897977,0.0006765008,0.00007777956,0.000009128278,0.000003812502],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8348988,"threshold_uncertainty_score":0.4313493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03848232709574564,"score_gpt":0.3287827867580378,"score_spread":0.2903004596622922,"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."}}