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Record W2028246521 · doi:10.12968/ijtr.2004.11.6.13333

Use of self-applied TENS for low back pain in people with multiple sclerosis

2004· article· en· W2028246521 on OpenAlexaboutno aff
K Warke, J Al-Smadi, David Baxter, DM Walsh, A S Lowe-Strong

Bibliographic record

VenueInternational Journal of Therapy and Rehabilitation · 2004
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsTranscutaneous electrical nerve stimulationPhysical therapyMedicinePlaceboRandomized controlled trialMultiple sclerosisPopulationClinical trialMcGill Pain QuestionnaireInternal medicineAlternative medicinePsychiatry

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the use of self-applied transcutaneous electrical nerve stimulation (TENS) for low back pain in a population of people with multiple sclerosis (MS). In total, 15 participants diagnosed with MS (aged 37–71 years) presenting with low back pain were recruited and randomized into two active TENS groups and a placebo group under double-blind conditions (n=5 per group). Treatment involved self-application of TENS on a daily basis for 6 weeks. Outcome measures were recorded at weeks 1, 6, 10 and 32. Statistical analysis indicated significant changes in the McGill Pain Questionnaire affective subsection (P=0.01) throughout the trial. However, trends observed seem to indicate an improvement in both active TENS groups during the treatment period, with differential improvement maintained into the follow-up period. The findings of this study thus warrant further investigation by means of a randomized, controlled clinical trial within this population.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.164

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.250
Teacher spread0.224 · 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

Citations10
Published2004
Admission routes1
Has abstractyes

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