Muscle oxygenation trends after tapering in trained cyclists
Bibliographic record
Abstract
BACKGROUND: This study examined muscle deoxygenation trends before and after a 7-day taper using non-invasive near infrared spectroscopy (NIRS). METHODS: Eleven cyclists performed an incremental cycle ergometer test to determine maximal oxygen consumption (VO2max = 4.68 +/- 0.57 L.min-1) prior to the study, and then completed two or three high intensity (85-90% VO2max) taper protocols after being randomly assigned to a taper group: T30 (n = 5), T50 (n = 5), or T80 (n = 5) [30%, 50%, 80% reduction in training volume, respectively]. Physiological measurements were recorded during a simulated 20 km time trials (20TT) performed on a set of wind-loaded rollers. RESULTS AND DISCUSSION: The results showed that the physiological variables of oxygen consumption (VO2), carbon dioxide (VCO2) and heart rate (HR) were not significantly different after tapering, except for a decreased ventilatory equivalent for oxygen (VE/VO2) in T50 (p </= 0.05). However, during the 20TT muscle deoxygenation measured continuously in the vastus medialis was significantly lower (-749 +/- 324 vs. -1140 +/- 465 mV) in T50 after tapering, which was concomitant with a 4.53% improvement (p = 0.057) in 20TT performance time, and a 0.18 L.min-1 (4.5%) increase in VO2. Furthermore, when changes in performance time and tissue deoxygenation (post- minus pre-taper) were plotted (n = 11), a moderately high correlation was found (r = 0.82). CONCLUSION: It was concluded that changes in simulated 20TT performance appeared to be related, in part, to changes in muscle deoxygenation following tapering, and that NIRS can be used effectively to monitor muscle deoxygenation during a taper period.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| 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".