Hyperhydration-Induced Decrease in Urinary Luteinizing Hormone Concentrations of Male Athletes in Doping Control Analysis
Bibliographic record
Abstract
Low urinary luteinizing hormone (LH) values have been discussed as a marker to detect steroid abuse. However, suppressed LH concentrations related to highly diluted urine samples could be a misleading indication of anabolic steroid abuse. One aim of the present study was to examine the effect of hyperhydration on the interpretation of LH findings during doping control analysis and to investigate different possibilities to correct volume-related changes in urinary LH concentrations. Seven healthy, physically active, nonsmoking White males were examined for a 72-hr period, using water and a commercial sports drink as hyperhydration agents (20 ml/kg body weight). Urine samples were collected and analyzed according to the World Anti-Doping Agency's technical documents. Baseline urinary LH concentrations, expressed as the mean ± SD for each individual, were within the acceptable physiological range (7.11 ± 5.42 IU/L). A comparison of the measured LH values for both hyperhydration phases (Phase A: 4.24 ± 5.60 IU/L and Phase B: 4.74 ± 4.72 IU/L) with the baseline ("normal") values showed significant differences (Phase A: p < .001 and Phase B: p < .001), suggesting the clear effect of urine dilution due to hyperhydration. However, an adjustment of urinary LH concentrations by specific gravity based on a reference value of 1.020 seems to adequately correct the hyperhydration-induced decrease on the LH levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 source (direct Gemma or distilled Codex), 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".