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Record W2676174492 · doi:10.1016/s0140-6736(16)32589-2

Guided graded exercise self-help plus specialist medical care versus specialist medical care alone for chronic fatigue syndrome (GETSET): a pragmatic randomised controlled trial

2017· article· en· W2676174492 on OpenAlexfundno aff
Lucy Clark, Francesca Pesola, Janice Thomas, Mario Vergara-Williamson, Michelle Beynon, Peter D. White

Bibliographic record

VenueThe Lancet · 2017
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsnot available
FundersBarts Health NHS TrustKing's College LondonNational Institute for Health and Care ResearchResearch for Patient Benefit ProgrammeKing's Health PartnersUnited Kingdom Clinical Research CollaborationLondon Community FoundationSouth London and Maudsley NHS Foundation Trust
KeywordsMedicineChronic fatigue syndromePhysical therapyRandomized controlled trialMedical careSpecialist careChronic fatigueFamily medicineInternal medicinePrimary care

Abstract

fetched live from OpenAlex

BACKGROUND: Graded exercise therapy is an effective and safe treatment for chronic fatigue syndrome, but it is therapist intensive and availability is limited. We aimed to test the efficacy and safety of graded exercise delivered as guided self-help. METHODS: In this pragmatic randomised controlled trial, we recruited adult patients (18 years and older) who met the UK National Institute for Health and Care Excellence criteria for chronic fatigue syndrome from two secondary-care clinics in the UK. Patients were randomly assigned to receive specialist medical care (SMC) alone (control group) or SMC with additional guided graded exercise self-help (GES). Block randomisation (randomly varying block sizes) was done at the level of the individual with a computer-generated sequence and was stratified by centre, depression score, and severity of physical disability. Patients and physiotherapists were necessarily unmasked from intervention assignment; the statistician was masked from intervention assignment. SMC was delivered by specialist doctors but was not standardised; GES consisted of a self-help booklet describing a six-step graded exercise programme that would take roughly 12 weeks to complete, and up to four guidance sessions with a physiotherapist over 8 weeks (maximum 90 min in total). Primary outcomes were fatigue (measured by the Chalder Fatigue Questionnaire) and physical function (assessed by the Short Form-36 physical function subscale); both were self-rated by patients at 12 weeks after randomisation and analysed in all randomised patients with outcome data at follow-up (ie, by modified intention to treat). We recorded adverse events, including serious adverse reactions to trial interventions. We used multiple linear regression analysis to compare SMC with GES, adjusting for baseline and stratification factors. This trial is registered at ISRCTN, number ISRCTN22975026. FINDINGS: Between May 15, 2012, and Dec 24, 2014, we recruited 211 eligible patients, of whom 107 were assigned to the GES group and 104 to the control group. At 12 weeks, compared with the control group, mean fatigue score was 19·1 (SD 7·6) in the GES group and 22·9 (6·9) in the control group (adjusted difference -4·2 points, 95% CI -6·1 to -2·3, p<0·0001; effect size 0·53) and mean physical function score was 55·7 (23·3) in the GES group and 50·8 (25·3) in the control group (adjusted difference 6·3 points, 1·8 to 10·8, p=0·006; 0·20). No serious adverse reactions were recorded and other safety measures did not differ between the groups, after allowing for missing data. INTERPRETATION: GES is a safe intervention that might reduce fatigue and, to a lesser extent, physical disability for patients with chronic fatigue syndrome. These findings need confirmation and extension to other health-care settings. FUNDING: UK National Institute for Health Research Research for Patient Benefit Programme and the Sue Estermann Fund.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.366
Teacher spread0.310 · 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.

Study designRandomized trial
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

Citations74
Published2017
Admission routes1
Has abstractyes

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