Exercise therapy for chronic low back pain: protocol for an individual participant data meta-analysis
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is one of the leading causes of disability and has a major socioeconomic impact. Despite a large amount of research in the field, there remains uncertainty about the best treatment approach for chronic LBP, and identification of relevant patient subgroups is an important goal. Exercise therapy is a commonly used strategy to treat chronic low back pain and is one of several interventions that evidence suggests is moderately effective.In parallel with an update of the 2005 Cochrane review, we will undertake an individual participant data (IPD) meta-analysis, which will allow us to standardize analyses across studies and directly derive results, and to examine differential treatment effects across individuals to estimate how patients' characteristics modify treatment benefit. METHODS/DESIGN: We will use standard systematic review methods advocated by the Cochrane Collaboration to identify relevant trials. We will include trials evaluating exercise therapy compared to any or no other interventions in adult non-specific chronic LBP. Our primary outcomes of interest include pain, functional status, and return-to-work/absenteeism. We will assess potential risk of bias for each study meeting selection criteria, using criteria and methods recommended by the Cochrane BRG.The original individual participant data will be requested from the authors of selected trials having moderate to low risk of bias. We will test original data and compile a master dataset with information about each trial mapped on a pre-specified framework, including reported characteristics of the study sample, exercise therapy characteristics, individual patient characteristics at baseline and all follow-up periods, subgroup and treatment effect modifiers investigated. Our analyses will include descriptive, study-level meta-analysis and meta-regression analyses of the overall treatment effect, and individual-level IPD meta-analyses of treatment effect modification. IPD meta-analyses will be conducted using a one-step approach where the IPD from all studies are modeled simultaneously while accounting for the clustering of participants with studies. DISCUSSION: We will analyze IPD across a large number of LBP trials. The resulting larger sample size and consistent presentation of data will allow additional analyses to explore patient-level heterogeneity in treatment outcomes and prognosis of chronic LBP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.082 | 0.125 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.084 | 0.011 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".