Performance, Detection, Contamination, Compliance, and Cointervention Biases in Rehabilitation Research
Bibliographic record
Abstract
ABSTRACT: Bias is a systematic error that can cause distorted results leading to incorrect conclusions. Intervention bias (i.e., contamination bias, cointervention bias, compliance bias, and performance bias) and detection bias are the most common biases in rehabilitation research. A better understanding of these biases is essential at all stages of research to enhance the quality of evidence in rehabilitation trials. Therefore, this narrative review aims to provide insights to the readers, clinicians, and researchers about contamination, cointervention, compliance, performance, and detection biases and ways of recognizing and mitigating them. 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.This review provides several strategies to guard against the impact of bias on study results. Clinicians, researchers, and other stakeholders are encouraged to apply these recommendations when designing and conducting rehabilitation trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.444 | 0.722 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".