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Record W2025209788 · doi:10.1016/s0304-3959(01)00274-3

Muscle pain inhibits cutaneous touch perception

2001· article· en· W2025209788 on OpenAlexaff
Christian S. Stohler, Charles J. Kowalski, James P. Lund

Bibliographic record

VenuePain · 2001
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMcGill University
FundersNational Institute of Dental and Craniofacial Research
KeywordsNociceptorHypoesthesiaTonic (physiology)MedicineSensationHypertonic salineNociceptionNoxious stimulusReferred painDiffuse noxious inhibitory controlThreshold of painSensory systemItchingAnesthesiaNeurosciencePsychologyDermatologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

The processing of noxious and non-noxious sensations differs between chronic pain syndromes, and we believe that studies of sensory processing in the presence of pain will help to clarify the aetiology of the conditions. Here we measured in humans the threshold-level mechanosensitivity in tonic experimental muscle pain. We found (1) that muscle pain induced by hypertonic saline reduced cutaneous threshold-level mechanosensitivity at the site of pain and at the mirror site in the contralateral face, (2) that this effect outlasted the sensation of pain, (3) that it was more pronounced when the painful area was reported to be large, and (4) that the loss of mechanosensitivity was greater in males than females. Comparing our findings to results obtained with other pain models, all classes of nociceptors do not seem to have the same effect on cutaneous mechanosensitivity. The observed threshold-level hypoesthesia is consistent with the hypothesis that the increased mechanical thresholds found in clinic cases of temporomandibular disorders and cervicobrachialgia are a direct result of the activation of muscle nociceptors.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0080.001

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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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 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

Citations71
Published2001
Admission routes1
Has abstractyes

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