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Record W2102295054 · doi:10.1123/mc.2013-0067

Acute Experimentally Induced Neck Pain Does Not Affect Fatigability of the Peripheral Biceps Brachii Muscle

2014· article· en· W2102295054 on OpenAlexaff
Laurie Y. Hung, Emmalee Maracle, John Srbely, Stephen H.M. Brown

Bibliographic record

VenueMotor Control · 2014
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBicepsIsometric exerciseMedicinePeripheralPhysical medicine and rehabilitationBiceps brachii muscleDelayed onset muscle sorenessElbowMuscle fatiguePlaceboAnesthesiaPhysical therapyElectromyographyAnatomyInternal medicineMuscle damage

Abstract

fetched live from OpenAlex

Evidence has shown that upper limb muscles peripheral to the cervical spine, such as the biceps brachii, can demonstrate functional deficits in the presence of chronic neck pain. However, few studies have examined how neck pain can affect the fatigability of upper limb muscles; therefore we were motivated to investigate the effects of acutely induced neuropathic neck pain on the fatigability of the biceps brachii muscle during isometric contraction to exhaustion. Topical capsaicin was used to induce neck pain in 11 healthy male participants. Surface EMG signals were recorded from the biceps brachii during an isometric elbow flexion fatigue task in which participants held a weight equivalent to 30% of their MVC until exhaustion. Two experimental sessions, one placebo and one capsaicin, were conducted separated by two days. EMG mean power frequency and average normalized activation values were calculated over the course of the fatigue task. In the presence of pain, there was no statistically significant effect on EMG parameters during fatigue of the biceps brachii. These results demonstrate that acutely induced neuropathic neck pain does not affect the fatigability, under the tested conditions, of the biceps brachii.

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)0.000
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 score0.670
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.009
GPT teacher head0.253
Teacher spread0.244 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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