A brief experimental examination of post-exercise hypotension and the impact of calculation method
Bibliographic record
Abstract
PURPOSE: There is great variability in the reported values of post-exercise hypotension (PEH), with inconsistent calculation methods employed across primary research. This study aimed to explore the influence of the mathematical calculation method on PEH variability, with the hypothesis that the method of identifying the lowest single reduction point (LSRP) would yield false-positive results. METHODS: Young, normotensive (108 ± 7/69 ± 5 mmHg), apparently healthy, male (n = 20) were included in this study. Participants completed three random-order experimental sessions, with blood pressure and heart rate measured before (10 min) and after (30 min) an acute bout of either isometric handgrip exercise, aerobic cycling, or a nonexercise control. Three PEH calculation methods were analyzed: LSRP, 30-min average across the full post-exercise recovery, and 15-min binned averages with two recovery windows (0-15 min, 15-30 min). RESULTS: The only calculation method to consistently identify PEH was the LSRP method, which identified PEH for SBP, DBP, and mean arterial pressure, across handgrip exercise, aerobic cycling, and even nonexercise control (P < 0.001). All other calculation methods inconsistently identified PEH across experimental sessions, supporting the hypothesis that LSRP inaccurately overreports PEH. CONCLUSION: Mathematical calculation method appears to be one source of variability contributing to the inconsistency in reported PEH among young, healthy males. This brief experimental examination reveals that the LSRP method should be avoided as it inaccurately overreports PEH. Alternatively, binned averages of smaller time windows across the recovery period may be a potentially advantageous approach and require further examination to determine to ideal level of granularity.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".