Effect of tapering period on plasma hormone concentrations, mood state, and performance of elite male cyclists
Bibliographic record
Abstract
Abstract In this study, we investigated the effect of 1‐ and 3‐week tapering periods on concentrations of plasma testosterone (T), cortisol (C), T/C ratio, mood state, and performance in elite male cyclists. After 8 weeks of progressive training, cyclists were randomly assigned to a control group (n=12) who continued performing intense training for a further 3 weeks, or a taper group (n=12) who continued with a 50% reduction in training volume. Blood testosterone and cortisol concentrations were assayed and the T/C ratio calculated from analysis obtained via standard ELISA. Mood state was determined using the Profile of Mood States (POMS) questionnaire. All data were collected immediately after a 40‐km time‐trial performed before, during, and after the 8‐week training protocol and after the 1‐ and 3‐week tapering/training periods. In the taper group, 40‐km time‐trial time decreased significantly (P<0.01) and equally for both the 1‐ and 3‐week taper periods relative to the control group. There were significant elevations in T/C ratio (P<0.001) and reductions in cortisol concentrations and POMS scores in the taper group relative to the control groups at the end of both the 1‐ and 3‐week tapering periods. Hence, taper periods are effective in improving performance and mood state and elevating the blood T/C ratio.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".