The influence of thermal factors on postexercise haemodynamics in trained and untrained men
Bibliographic record
Abstract
Previous work suggests that the cardiovascular mechanisms underlying the postexercise reduction in mean arterial pressure (MAP) differ between exercise‐trained and untrained men. However, these studies compared responses based on the same percentage of peak oxygen consumption (VO 2peak ) resulting in different rates of heat production during exercise. Thus, we evaluated if exercise‐induced differences in the requirement for heat loss, and therefore sweat loss, may influence postexercise haemodynamics. Twelve trained (T 60 ) (VO 2peak, 64 ± 4 mL/kg/min) and twelve untrained (UT 60 ) (VO 2peak, 49 ± 3 mL/kg/min) males, individually matched for body surface area, cycled for 60 min at 60% of VO 2peak . On a separate day the untrained group performed an additional 60 min of cycling at the same heat production as their trained match in T 60 (UT Match ) . Sweat loss was assessed by change in body weight (ΔBW). MAP and cardiac output (CO) were measured at baseline and every 10 min during 60 min of postexercise recovery. ΔBW was similar between T 60 and UT Match (T 60 : ‐1.32 ± 0.36 kg; UT Match : ‐1.32 ± 0.32 kg) and greater in these conditions relative to UT 60 (‐0.95 ± 0.13 kg; both P < 0.05). Further, MAP was ~5 mmHg below baseline following 20 min of recovery in each condition (all P < 0.05). Postexercise CO was ~7% below baseline in T 60 ( P < 0.05) whereas it was elevated relative to T 60 (both P < 0.05) and similar to baseline (both P > 0.05) in UT 60 and UT Match . We show that the differing mechanisms between trained and untrained males underlying the postexercise reduction in MAP are not the result of differences in exercise‐induced heat load and therefore fluid loss. Support: Natural Sciences and Engineering Research Council of Canada.
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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 | 0.000 | 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".