Observation of limb movements reduces phantom limb pain in bilateral amputees
Bibliographic record
Abstract
BACKGROUND: Mirror therapy has been demonstrated to reduce phantom limb pain (PLP) experienced by unilateral limb amputees. Research suggests that the visual feedback of observing a limb moving in the mirror is critical for therapeutic efficacy. OBJECTIVE: Since mirror therapy is not an option for bilateral lower limb amputees, the purpose of this study was to determine if direct observation of another person's limbs could be used to relieve PLP. METHODS: We randomly assigned 20 bilateral lower limb amputees with PLP to visual observation (n = 11) or mental visualization (n = 9) treatment. Treatment consisted of seven discrete movements which were mimicked by the amputee's phantom limbs moving while visually observing the experimenter's limbs moving, or closing the eyes while visualizing and attempting the movements with their phantom limbs, respectively. Participants performed movements for 20 min daily for 1 month. Response to therapy was measured using a 100-mm visual analog scale (VAS) and the McGill Short-Form Pain Questionnaire (SF-MPQ). RESULTS: Direct visual observation significantly reduced PLP in both legs (P < 0.05). Amputees assigned to the mental visualization condition did not show a significant reduction in PLP. INTERPRETATION: Direct visual observation therapy is an inexpensive and effective treatment for PLP that is accessible to bilateral lower limb amputees.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".