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Acupuncture, Transcutaneous Electrical Nerve Stimulation, and Laser Therapy in Chronic Pain

2001· review· en· W2071155573 on OpenAlexaff
Angelica Fargas-Babjak

Bibliographic record

VenueClinical Journal of Pain · 2001
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMyofascial pain diagnosis and treatment
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsAcupunctureMedicineTranscutaneous electrical nerve stimulationRandomized controlled trialStimulationAnalgesicChronic painAnesthesiaPhysical therapySurgeryAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this review was to determine how effective acupuncture, transcutaneous electrical nerve stimulation, acupuncture-like transcutaneous nerve stimulation, laser therapy, electrical nerve stimulation, and neuroreflexotherapy are in the management of chronic pain. METHODOLOGY: The literature search identified six systematic reviews of the literature and four randomized controlled trials to provide evidence for this review. RESULTS: The systematic reviews included different methodologies and heterogeneity of study groups, but studies were generally of poor methodology. Although sham acupuncture may have analgesic effects, it was used as a control in many studies. CONCLUSIONS: In general, the evidence was contradictory or inadequate, reflecting poor study methodologies. No positive conclusion could be reached for acupuncture, transcutaneous electrical nerve stimulation, acupuncture-like transcutaneous nerve stimulation, laser therapy, or neuroreflexotherapy. A single randomized controlled trial provided limited evidence (level 3) that electrical nerve stimulation is effective for pain relief in myofascial pain syndrome for up to 4 weeks, but further study in humans is needed. Future randomized controlled trials and systematic reviews should include subgroup analyses of sham acupuncture and inert placebos as controls.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.994
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.053
GPT teacher head0.389
Teacher spread0.337 · 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 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

Citations41
Published2001
Admission routes1
Has abstractyes

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