The influence of bias in randomized controlled trials on rehabilitation intervention effect estimates: what we have learned from meta-epidemiological studies
Bibliographic record
Abstract
This study aimed to synthesize evidence from studies that addressed the influence of bias domains in randomized controlled trials on rehabilitation intervention effect estimates and discuss how these findings can maximize the trustworthiness of an RCT in rehabilitation. We screened studies about the influence of bias on rehabilitation intervention effect estimates published until June 2023. The characteristics and results of the included studies were categorized based on methodological characteristics and summarized narratively. We included seven studies with data on 227,806 RCT participants. Our findings showed that rehabilitation intervention effect estimates are likely exaggerated in trials with inadequate/unclear sequence generation and allocation concealment when using continuous outcomes. The influence of blinding was inconsistent and different from the rest of medical science, as meta-epidemiological studies showed overestimation, underestimation, or neutral associations for different types of blinding on rehabilitation treatment effect estimates. Still, it showed a more consistent pattern when looking at patient-reported outcomes. The impact of attrition bias and intention to treat has been analyzed only in two studies with inconsistent results. The risk of reporting bias seems to be associated with overestimation of treatment effects. Bias domains can influence rehabilitation treatment effects in different directions. The evidence is mixed and inconclusive due to the poor methodological quality of RCTs and the limited number and quality of studies looking at the influence of bias and treatment effects in rehabilitation. Further studies about the influence of bias in RCTs on rehabilitation intervention effect estimates are needed.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad)Meta-epidemiology (narrow) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | medium |
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 | 0.498 | 0.797 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.023 | 0.022 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".