Selection, Confounding, and Attrition Biases in Randomized Controlled Trials of Rehabilitation Interventions
Bibliographic record
Abstract
ABSTRACT: A thorough knowledge of biases in intervention studies and how they influence study results is essential for the practice of evidence-based medicine. The objective of this review was to provide a basic knowledge and understanding of the concept of biases and associated influence of these biases on treatment effects, focusing on the area of rehabilitation research. This article provides a description of selection biases, confounding, and attrition biases. In addition, useful recommendations are provided to identify, avoid, or control these biases when designing and conducting rehabilitation trials. The literature selected for this review was obtained mainly by compiling the information from several reviews looking at biases in rehabilitation. In addition, separate searches by biases and looking at reference lists of selected studies as well as using Scopus forward citation for relevant references were used. If not addressed appropriately, biases related to intervention research are a threat to internal validity and consequently to external validity. By addressing these biases, ensuring appropriate randomization, allocation concealment, appropriate retention techniques to avoid dropouts, appropriate study design and statistical analysis, among others, will generate more accurate treatment effects. Based on their impact on clinical results, a proper understanding of these concepts is central for researchers, rehabilitation clinicians, and other stakeholders working on this field.
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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 | 0.423 | 0.652 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| 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, 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".