{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007939289,0.0005357231,0.0004397008,0.001211025,0.0002874345,0.0004726717,0.000395048,0.0004531902,0.000462905],"category_scores_gemma":[0.0005071483,0.000303978,0.0002569623,0.0004823515,0.0003765038,0.0003398638,0.0004060073,0.0004045363,0.0002652435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002408464,"about_ca_system_score_gemma":0.0003409388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003364748,"about_ca_topic_score_gemma":0.0006194568,"domain_scores_codex":[0.9996002,0.0001237054,0.00002260681,0.00009081572,0.000142387,0.00002027436],"domain_scores_gemma":[0.9998333,0.0000440059,0.0000374168,0.0000171256,0.00004522104,0.00002291339],"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":[0.0007228481,0.0001492816,0.005832474,0.0002550432,0.0001204565,0.0003455312,0.0000489909,0.0003463613,0.8887163,0.0002594356,0.0004261186,0.1027773],"study_design_scores_gemma":[0.0002957221,0.005105234,0.1411773,0.0001199637,0.0006030513,0.02664867,0.0002736232,0.03975397,0.7581849,0.003714034,0.02391576,0.0002078569],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4863442,0.02298207,0.4821061,0.001381237,0.0002722164,0.0005968655,0.0007367751,0.001535453,0.004045012],"genre_scores_gemma":[0.5330696,0.005054532,0.4581706,0.0007024218,0.0001525256,0.000281009,0.000388071,0.00004348132,0.002137813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001211025,"threshold_uncertainty_score":0.00419879,"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."}}