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Record W3148686203

Effects of Intramuscular Stimulation in Myofascial Pain Syndrome of Upper Trapezius Muscle

2003· article· en· W3148686203 on OpenAlexaboutno aff
Hwan-Taek Byeon, Seong-Hee Park, Myoung-Hwan Ko, Jeong‐Hwan Seo

Bibliographic record

VenueAnnals of Rehabilitation Medicine · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMyofascial pain diagnosis and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDry needlingMyofascial pain syndromeTrapezius muscleVisual analogue scaleStimulationMyofascial painPhysical therapyAnesthesiaPhysical medicine and rehabilitationElectromyographyInternal medicineAcupuncturePathologyAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective: This study was conducted to assess effects of intramuscular stimulation (IMS) in comparison with that of dry needling and intramuscular electrical stimulation (IMES) in the patients with myofascial pain syndrome (MPS) of upper trapezius. Method: Thirty patients with MPS were assigned randomly to three groups, such as dry needling group (n=10), IMES group (n=10), and IMS group (n=10). In dry needling group, dry needling was applied to the trigger point of upper trapezius muscle. In IMES group, IMES was applied to the trigger point of upper trapezius muscle. In IMS group, IMS was applied to the trigger point of upper trapezius and parcervical muscles. Treatment were done three times a week for 2 weeks. Effects were assessed on 3rd day, 7th day and 14th day after treatment by visual analogue scale (VAS), McGill pain questionnaire (MPQ), and passive range of motion (PROM) of cervical spine. Results: Significant changes of VAS and PROM were noticed in IMS group, compared with other groups. No significant difference of MPQ was noticed in IMS group, compared with other groups. Conclusion: IMS may be more effective treatment modality than dry needling and IMES in patients with MPS of upper trapezius muscle.

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)0.004
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 score0.584
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.010
GPT teacher head0.286
Teacher spread0.276 · 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

Citations6
Published2003
Admission routes1
Has abstractyes

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