How does the risk of bias influence the effect sizes of exercise therapy in chronic low back pain randomised controlled trials? A protocol for a meta-epidemiological study
Bibliographic record
Abstract
ABSTRACT Background and Objectives Risk of bias is a critical issue to consider when appraising studies. Generally, the higher the risk of bias of a study, the less confidence there will be that the results are valid. Considering that low back pain is recognized to have an extremely high disease burden; exercise therapy is one of the most frequently prescribed interventions for chronic low back pain (CLBP) and that most low back pain trials have methodological limitations that could bias treatment effect estimates; the objective of this study is to explore causal pathways between the sources of risk of bias and estimates of the treatment effect of exercise therapy interventions in CLBP trials. Methods The 249 RCTs included in the 2021 Cochrane review publication “Exercise therapy for chronic low back pain” will be included. The risk of bias will be evaluated with the Cochrane Risk of Bias 2 tool (ROB 2). Causal pathways between the exposure (risk of bias domains) and our outcomes of interest (effect sizes for pain and functional limitations) will be explored through univariable and multivariable meta-regression models. These models will be adjusted for potential confounders (sample size, trial registration, incomplete flow chart information and treatment comparisons), exploring relevant interactions within each model. Additional and sensitivity analyses will be performed to explore and test the robustness of the primary analyses. Ethics and dissemination A manuscript will be prepared and submitted for publication in an appropriate peer-reviewed journal upon study completion. We believe that the results of this investigation will be relevant to researchers paying more attention to the synthesis of the evidence to translate clinical implications to key stakeholders (healthcare providers and patients).
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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 | 0.394 | 0.572 |
| Meta-epidemiology (narrow) | 0.009 | 0.007 |
| Meta-epidemiology (broad) | 0.024 | 0.059 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.024 | 0.021 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".