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Record W2099929166 · doi:10.3109/09593985.2010.502554

The search for pain relief in people with chronic fatigue syndrome: A descriptive study

2010· article· en· W2099929166 on OpenAlexfundno aff
Rebecca Marshall, Lorna Paul, Les Wood

Bibliographic record

VenuePhysiotherapy Theory and Practice · 2010
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsnot available
FundersUniversity of GlasgowGlasgow Caledonian UniversityMcGill University
KeywordsMedicineAcupuncturePhysical therapyPsychological interventionPain reliefChronic painAlternative medicinePsychiatrySurgery

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the use and perceived benefit of complimentary and alternative medicine (CAM) and physiotherapy treatments tried by people with chronic fatigue syndrome (CFS) to ease painful symptoms. This study used a descriptive, cross-sectional design. People with CFS who experienced pain were recruited to this study. Participants were asked during a semistructured interview about the treatments they had tried to relieve their pain. Each interview was conducted in the home of the participant. Fifty participants were recruited, of which, 10 participants were severely disabled by CFS. Eighteen participants were trying different forms of CAM treatment for pain relief at the time of assessment. Three participants were currently receiving physiotherapy. Throughout the duration of their illness 45 participants reported trying 19 different CAM treatments in the search for pain relief. Acupuncture was reported to provide the most pain relief (n=16). Twenty-seven participants reported a total of 16 different interventions prescribed by their physiotherapist. The results of this study suggest some physiotherapy and CAM treatments may help people manage painful CFS symptoms. Future research should be directed to evaluating the effectiveness of interventions such as acupuncture or gentle soft tissue therapies to reduce pain in people with CFS.

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)0.008
metaresearch head score (Gemma)0.001
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 score0.450
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
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 integrity0.0000.001
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 head0.022
GPT teacher head0.356
Teacher spread0.335 · 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

Citations16
Published2010
Admission routes1
Has abstractyes

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