Speeding of V<scp>o</scp><sub>2</sub> kinetics during moderate-intensity exercise subsequent to heavy-intensity exercise is associated with improved local O<sub>2</sub> distribution
Bibliographic record
Abstract
The relationship between the adjustment of muscle deoxygenation (Δ[HHb]) and phase II V(O(2p)) during moderate-intensity exercise was examined before (Mod 1) and after (Mod 2) a bout of heavy-intensity "priming" exercise. Moderate intensity V(O(2p)) and Δ[HHb] kinetics were determined in 18 young males (26 ± 3 yr). V(O(2p)) was measured breath-by-breath. Changes in Δ[HHb] of the vastus lateralis muscle were measured by near-infrared spectroscopy. V(O(2p)) and Δ[HHb] response profiles were fit using a monoexponential model, and scaled to a relative % of the response (0-100%). The Δ[HHb]/Vo(2) ratio for each individual (reflecting the local matching of O(2) delivery to O(2) utilization) was calculated as the average Δ[HHb]/Vo(2) response from 20 s to 120 s during the exercise on-transient. Phase II τV(O(2p)) was reduced in Mod 2 compared with Mod 1 (P < 0.05). The effective τ'Δ[HHb] remained the same in Mod 1 and Mod 2 (P > 0.05). During Mod 1, there was an "overshoot" in the Δ[HHb]/Vo(2) ratio (1.08; P < 0.05) that was not present during Mod 2 (1.01; P > 0.05). There was a positive correlation between the reduction in the Δ[HHb]/Vo(2) ratio and the smaller τV(O(2p)) from Mod 1 to Mod 2 (r = 0.78; P < 0.05). This study showed that a smaller τV(O(2p)) during a moderate bout of exercise subsequent to a heavy-intensity priming exercise was associated with improved microvascular O(2) delivery during the on-transient of exercise, as suggested by a smaller Δ[HHb]/Vo(2) 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 | 0.000 |
| 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 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".