The kinetics of blood lactate in boys during and following a single and repeated all-out sprints of cycling are different than in men
Bibliographic record
Abstract
This study characterized the impact of high-intensity interval training on the kinetics of blood lactate and performance in trained boys and men. Twenty-one boys (11.4 ± 0.8 years) and 19 men (29.4 ± 5.0 years) performed a set of four 30-s sprints with 2-min of rest and a single 30-s sprint on 2 separate occasions (randomized order) with assessment of performance. Blood lactate was assayed after each sprint and during 30 min of recovery from both tests. The individual time-curves of blood lactate concentration were fitted to the biexponential function as follows: [Formula: see text], where the velocity parameters γ1 and γ2 reflect the capacity to release lactate from the previously active muscle into the blood and to subsequently eliminate lactate from the organism, respectively. In both tests, peak blood lactate concentration was significantly lower in the boys (four 30-s sprints: 12.2 ± 3.6 mmol·L(-1); single 30-s sprint: 8.7 ± 1.8 mmol·L(-1)) than men (four 30-s sprints: 16.1 ± 3.3 mmol·L(-1); single 30-s sprint: 11.5 ± 2.1; p < 0.001). The boys exhibited faster γ1 (1.4531 ± 0.65 min; p < 0.001) and γ2 (0.059 ± 0.023 min; p = 0.01) in the single 30-s sprint and faster γ2 (0.049 ± 0.016 min; p = 0.01) in the four 30-s sprints. The worsening of performance from the first to the last of the four 30-s sprints was less pronounced in boys (9.2% ± 13.9%) than men (19.2% ± 11.5%; p = 0.01). In the present study boys, when compared with men, exhibited lower Peak blood lactate concentration; less pronounced decline in performance during the sprints concomitantly with more rapid release and elimination during the single 30-s sprint; and faster elimination of lactate following the four 30-s sprints.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".