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Record W4321189893 · doi:10.14814/phy2.15524

Post‐exercise hypotension in male spontaneously hypertensive rats: The issue of calculation method

2023· article· en· W4321189893 on OpenAlexaff
A. Krzesiak, Julie Lavoie, Stéphane Sebille, Christian Cognard, Laurent Bosquet, N. Delpech

Bibliographic record

VenuePhysiological Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineCardiologyBlood pressureTreadmillAerobic exerciseInternal medicineIntensity (physics)CuffPhysical therapySurgery

Abstract

fetched live from OpenAlex

In spontaneously hypertensive rats, exercise can lead to a post-exercise decrease in blood pressure, named post-exercise hypotension (PEH). This can be following physical training but also after a single bout of mild to moderate exercise when measured with tail-cuff or externalized catheter methods. Our aim was to assess the PEH obtained with different calculation methods and to compare the magnitude of this effect induced by a moderate-intensity continuous exercise or a high-intensity intermittent exercise. Thirteen 16-week-old male spontaneously hypertensive rats performed two types of aerobic exercise (continuous or intermittent) on a treadmill. Arterial pressure was recorded by telemetry for 24 h which was started 3 h before physical exercise. Based on the literature, PEH was first evaluated with two different baseline values, and then with three different approaches. We observed that the identification of PEH depended on the method used to measure the rest value, and that its amplitude was also influenced by the calculation approach and the type of exercise performed. Hence, the calculation method and the amplitude of the detected PEH can significantly influence their physiological and pathophysiological inferences.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.023
GPT teacher head0.298
Teacher spread0.275 · 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 designBench or experimental
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

Citations2
Published2023
Admission routes1
Has abstractyes

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