Urinary human chorionic gonadotropin isoform concentrations in doping control samples
Bibliographic record
Abstract
Anti-doping laboratories routinely use immunoassays to measure urinary concentrations of human chorionic gonadotropin (hCG). To minimize immunoassay differences and false positive screen results from inactive isoforms (free β-subunit (hCGβ), β-subunit core fragment (hCGβcf)) laboratories now use intact hCG instead of total hCG immunoassays to measure hCG. To determine the distribution of hCG isoforms in urine, we determined the concentrations of intact hCG, hCGβ, and hCGβcf in male urine samples based on immunoassay total hCG concentrations using a sequential immunoextraction and a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. hCG was isolated using antibody-conjugated magnetic beads and unique tryptic peptides were quantified by LC-MS/MS. Negative samples with detectable but low total hCG concentrations (1.2-3.5 pmol/L) had intact and hCGβ concentrations <1.2 pmol/L, and hCGβcf concentrations <2.3 pmol/L by LC-MS/MS. Urine samples from an athlete receiving hCG had intact hCG concentrations ranging from 18.8 to 57.6 pmol/L, hCGβ concentrations <0.7 pmol/L, and hCGβcf concentrations ranging from 94 to 243% of the intact hCG concentration. In 27 atypical samples with total hCG concentrations ranging from 16.7 to 412.7 pmol/L with intact hCG <2.7 pmol/L by immunoassay, all samples had intact hCG concentrations <3.8 pmol/L and hCGβ concentrations <6.2 pmol/L by LC-MS/MS. hCGβcf concentrations by LC-MS/MS varied widely and ranged from 1.03 to 21.9 pmol/L. In summary, total hCG immunoassays significantly overestimate hCG concentrations and can produce false positive results. Although the intact hCG immunoassay slightly overestimates hCG concentrations compared to LC-MS/MS, it can distinguish between cases of hCG use and atypical cases with elevated total hCG concentrations. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".