The effect of bodily illusions on clinical pain
Bibliographic record
Abstract
This systematic review and meta-analysis critically examined the evidence for bodily illusions to modulate pain. Six databases were searched; 2 independent reviewers completed study inclusion, risk of bias assessment, and data extraction. Included studies evaluated the effect of a bodily illusion on pain, comparing results with a control group/condition. Of the 2213 studies identified, 20 studies (21 experiments) were included. Risk of bias was high due to selection bias and lack of blinding. Consistent evidence of pain decrease was found for illusions of the existence of a body part (myoelectric/Sauerbruch prosthesis vs cosmetic/no prosthesis; standardized mean differences = -1.84, 95% CI = -2.67 to -1.00) and 4 to 6 weeks of mirror therapy (standardized mean differences = -1.11, 95% CI = -1.66 to -0.56). Bodily resizing illusions had consistent evidence of pain modulation (in the direction hypothesized). Pooled data found no effect on pain for 1 session of mirror therapy or for incongruent movement illusions (except for comparisons with congruent mirrored movements: incongruent movement illusion significantly increased the odds of experiencing pain). Conflicting results were found for virtual walking illusions (both active and inactive control comparisons). Single studies suggest no effect of resizing illusions on pain evoked by noxious stimuli, no effect of embodiment illusions, but a significant pain decrease with synchronous mirrored stroking in nonresponders to traditional mirror therapy. There is limited evidence to suggest that bodily illusions can alter pain, but some illusions, namely mirror therapy, bodily resizing, and use of functional prostheses show therapeutic promise.
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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 | 0.022 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".