Muscle oxygenation trends after tapering in trained cyclists
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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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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 | 0.000 |
| 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) | 0.001 | 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 it