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Mean arterial pressure following prolonged exercise in the heat: <scp>I</scp>nfluence of training status and fluid replacement

2012· article· en· W2141619734 on OpenAlexafffund
Daniel Gagnon, Aaron Lynn, Konrad Binder, Robert Boushel, Glen P. Kenny

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2012
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsFluid replacementMean arterial pressureMedicineBlood pressureHyperthermiaCardiologyInternal medicineAnesthesiaHeart rate

Abstract

fetched live from OpenAlex

Prolonged exercise in the heat without fluid replacement represents a significant challenge to the regulation of mean arterial pressure (MAP). It is unknown, however, if MAP is equally challenged during the post-exercise period, and whether regular endurance exercise training can provide any benefit to its regulation. We examined MAP (Finometer) in eight trained (T) and eight untrained (UT) individuals prior to, and following, 120 min of cycling at 42 °C with (HYD) and without (DEHY) fluid replacement. Exercise during DEHY induced significant hyperthermia (T: 39.20 ± 0.52 °C vs UT: 38.70 ± 0.36 °C, P = 0.941) and body weight losses (T: 3.4 ± 1.2% vs UT: 2.7 ± 0.9%, P = 0.332), which did not differ between groups. Although MAP was equally reduced 5 min into the post-exercise period of DEHY (T: -20 ± 11 mmHg vs UT: -22 ± 13 mmHg, P = 0.800), its subsequent recovery was significantly different between groups (P = 0.037). While MAP returned to pre-exercise values in UT (-1 ± 3 mmHg), it remained reduced in T (-9 ± 3 mmHg, P = 0.028). No differences in MAP post-exercise were observed between groups during HYD. These data suggest that trained men exhibit a greater level of post-exercise hypotension following prolonged exercise in the heat without fluid replacement. Furthermore, fluid replacement reverses the sustained post-exercise hypotension observed in trained individuals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.317
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2012
Admission routes2
Has abstractyes

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