Bias in the measurement of the outcome is associated with effect sizes in randomized clinical trials on exercise therapy for chronic low back pain: a meta-epidemiological study
Bibliographic record
Abstract
OBJECTIVES: To explore the relationships between the risk of bias and treatment effect estimates for exercise therapy interventions on pain intensity and physical functioning outcomes in randomized controlled trials (RCTs) involving patients with chronic low back pain. STUDY DESIGN AND SETTING: A cross-sectional meta-epidemiological study of the 230 RCTs (31,674 participants) in the 2021 'Exercise therapy for chronic low back pain' Cochrane Review were included. Study design characteristics, sample size, prospective trial registration, flowchart information, interventions, and comparisons were extracted. Independent pairs of reviewers assessed the risk of bias using the Cochrane Risk of Bias 2 tool. RESULTS: The metaregression included 220 (pain intensity) and 203 (physical functioning) effect sizes. Unadjusted and adjusted metaregression models showed no significant associations between the bias domains and pain intensity effect sizes. Only domain 'bias in the measurement of the outcome' was significantly associated with physical functioning (standardized mean difference: -0.40, 95% confidence interval: -0.77 to -0.02) when adjusted for flowchart reported (yes/no), prospective trial registration, sample size, and comparator type. CONCLUSION: The risk of bias in the measurement of the outcome could lead to slight overestimates of the effect size for physical functioning. Clinicians should consider this when they read and assess RCT results in this field. We encourage metaresearchers to replicate our findings using a consistent approach for evaluating the risk of bias (i.e., the RoB 2 tool) in other musculoskeletal conditions and interventions to investigate their generalizability.
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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 | 0.393 | 0.655 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.023 | 0.073 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".