Effect of differences in post-exercise lactate accumulation in athletes’ haemodynamics
Bibliographic record
Abstract
To verify the relationship between exercise intensity and post-exercise haemodynamics, we studied haemodynamic and lactate responses during 10 min following 3 bicycle tests. Two tests were performed for 3 min at 70% and 130% of the workload corresponding to anaerobic threshold (70% W(at) and 130% W(at) tests), and 1 was performed until exhaustion at 150% of the maximum workload achieved during a previous incremental test (150% W(max) test). During the recovery period after the 150% W(max) test we observed the highest increases in blood lactate with respect to the baseline: at the 9th minute of recovery lactate concentration increased by +9.3 +/- 2.7, +6.4 +/- 3.1, and +1.1 +/- 0.9 mmol x L(-1) in the 150% W(max) (p > 0.05 with respect to the other protocol sessions), 130% W(at), and 70% W(at) tests, respectively. We also observed greater reductions in cardiac pre-load and systemic vascular resistance in the 150% W(max) test than in the 130% W(at) and 70% W(at) tests. However, the cardiac output response successfully faced the increased vasodilatation occurring during 150% W(max) test so that changes in mean blood pressure were similar in the 3 test conditions. This study shows that exercises that yielded different lactate concentrations also led to greater vasodilatation. Nevertheless, mechanisms controlling the cardiovascular apparatus successfully prevented a drop in blood pressure in spite of the cardiovascular stress.
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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 | 0.001 |
| 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".