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Obligatory neural feedback control of exercise cardiorespiratory function and performance

2025· review· en· W4415826648 on OpenAlexaff
Jerome A. Dempsey, Barbara J. Morgan

Bibliographic record

VenueJournal of Applied Physiology · 2025
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsThe Quebec Population Health Research Network
FundersNational Heart, Lung, and Blood Institute
KeywordsCardiorespiratory fitnessHyperpneaPhysical exerciseRespiratory systemControl of respirationSensory systemMotor control

Abstract

fetched live from OpenAlex

Our review addresses the contributions of four sources of neural sensory feedback, namely, locomotor and respiratory muscle afferents, lung stretch receptors, and carotid chemoreceptors, to the cardiorespiratory responses to rhythmic exercise and to exercise limitation in health and disease. Experiments in healthy humans and animals that used a blockade or partial blockade of each feedback mechanism during physiological exercise demonstrated the obligatory nature of each of these sensory inputs to cardiorespiratory function, locomotor effort and muscle fatigue, and exercise performance. More recent research has revealed enhanced contributions of these feedback influences to exercise cardiorespiratory function in chronic heart failure, chronic obstructive pulmonary disease, and hypertension. Future research needs to address: 1) how these obligatory neural feedback mechanisms might interact with a raised respiratory CO 2 exchange in regulating the hyperpnea of exercise; and 2) the pathogenesis of feedback hypersensitivity in chronic diseases and the means and therapeutic benefits of its normalization.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.027
GPT teacher head0.276
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

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