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Record W4200564439 · doi:10.31189/2165-6193-10.4.150

Effects of Acute and Chronic Stretching on Pain Control

2021· article· en· W4200564439 on OpenAlexaff
David G. Behm, Anthony D Kay, Gabriel S. Trajano, Shahab Alizadeh, Anthony J. Blazevich

Bibliographic record

VenueJournal of Clinical Exercise Physiology · 2021
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineReflexChronic painPhysical medicine and rehabilitationStretch reflexAcute painPain toleranceSensory systemPhysical therapyAnesthesiaThreshold of painNeurosciencePsychology

Abstract

fetched live from OpenAlex

ABSTRACT While muscle stretching has been commonly used to alleviate pain, reports of its effectiveness are conflicting. The objective of this review is to investigate the acute and chronic effects of stretching on pain, including delayed onset muscle soreness. The few studies implementing acute stretching protocols have reported small to large magnitude decreases in quadriceps and anterior knee pain as well as reductions in headache pain. Chronic stretching programs have demonstrated more consistent reductions in pain from a wide variety of joints and muscles, which has been ascribed to an increased sensory (pain) tolerance. Other mechanisms underlying acute and chronic pain reduction have been proposed to be related to gate control theory, diffuse noxious inhibitory control, myofascial meridians, and reflex-induced increases in parasympathetic nervous activity. By contrast, the acute effects of stretching on delayed onset muscle soreness are conflicting. Reports of stretch-induced reductions in delayed onset muscle soreness may be attributed to increased pain tolerance or alterations in the muscle's parallel elastic component or extracellular matrix properties providing protection against tissue damage. Further research evaluating the effect of various stretching protocols on different pain modalities is needed to clarify conflicts within the literature.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.352
Teacher spread0.341 · 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

Citations34
Published2021
Admission routes1
Has abstractyes

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