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Record W2991244705 · doi:10.1136/bjsports-2019-101205

Exercise treatment effect modifiers in persistent low back pain: an individual participant data meta-analysis of 3514 participants from 27 randomised controlled trials

2019· review· en· W2991244705 on OpenAlexafffund
Jill A. Hayden, Maria Wilson, Samuel A. Stewart, Jennifer Cartwright, Andrea O Smith, Richard D Riley, Maurits W. van Tulder, Tom Bendix, Francesca Cecchi, Leonardo Oliveira Pena Costa, Ninna Dufour, Manuela L. Ferreira, Nadine E. Foster, Maruti R. Gudavalli, Jan Hartvigsen, Pieter H. Helmhout, Jan Kool, George Α. Koumantakis, Francisco M. Kovacs, Tiina Kuukkanen, Audrey Long, Luciana Macedo, Luciana A. C. Machado, Christopher G. Maher, Wolf Mehling, Giovanni Morone, Tom Peterson, Eva Rasmussen-Barr, Cormac Ryan, Tuulikki Sjögren, Rob Smeets, J. Bart Staal, Monica Unsgaard‐Tøndel, Henry Wajswelner, Ella W. Yeung

Bibliographic record

VenueBritish Journal of Sports Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster UniversityDalhousie University
FundersNational Institute for Health and Care ResearchResearch Nova ScotiaNova Scotia Health Research Foundation
KeywordsMedicinePhysical therapyAlternative medicineBack painLow back painRandomized controlled trialMeta-analysisPhysical medicine and rehabilitationSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Low back pain is one of the leading causes of disability worldwide. Exercise therapy is widely recommended to treat persistent non-specific low back pain. While evidence suggests exercise is, on average, moderately effective, there remains uncertainty about which individuals might benefit the most from exercise. METHODS: In parallel with a Cochrane review update, we requested individual participant data (IPD) from high-quality randomised clinical trials of adults with our two primary outcomes of interest, pain and functional limitations, and calculated global recovery. We compiled a master data set including baseline participant characteristics, exercise and comparison characteristics, and outcomes at short-term, moderate-term and long-term follow-up. We conducted descriptive analyses and one-stage IPD meta-analysis using multilevel mixed-effects regression of the overall treatment effect and prespecified potential treatment effect modifiers. RESULTS: We received IPD for 27 trials (3514 participants). For studies included in this analysis, compared with no treatment/usual care, exercise therapy on average reduced pain (mean effect/100 (95% CI) -10.7 (-14.1 to -7.4)), a result compatible with a clinically important 20% smallest worthwhile effect. Exercise therapy reduced functional limitations with a clinically important 23% improvement (mean effect/100 (95% CI) -10.2 (-13.2 to -7.3)) at short-term follow-up. Not having heavy physical demands at work and medication use for low back pain were potential treatment effect modifiers-these were associated with superior exercise outcomes relative to non-exercise comparisons. Lower body mass index was also associated with better outcomes in exercise compared with no treatment/usual care. This study was limited by inconsistent availability and measurement of participant characteristics. CONCLUSIONS: This study provides potentially useful information to help treat patients and design future studies of exercise interventions that are better matched to specific subgroups. PROTOCOL PUBLICATION: https://doi.org/10.1186/2046-4053-1-64.

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)0.047
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (broad)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.647
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0470.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0410.011
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.336
GPT teacher head0.426
Teacher spread0.090 · 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; both teacher heads agree on what is shown here.

Study designMeta-analysis
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

Citations143
Published2019
Admission routes2
Has abstractyes

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