Decreasing Phantom Limb Pain Through Observation of Action and Imagery: A Case Series
Bibliographic record
Abstract
BACKGROUND: Phantom limb pain is often resistant to treatment. Techniques based on visual-kinesthetic feedback could help reduce it. OBJECTIVE: The objective of the current study was to test if a novel intervention combining observation and imagination of movements can reduce phantom limb pain. METHODS: This single-case multiple baseline study included six persons with upper or lower limb phantom pain. Participants' pain and imagery abilities were assessed by questionnaires. After a 3-5-week baseline, participants received a two-step intervention of 8 weeks. Intervention 1 was conducted at the laboratory with a therapist (two sessions/week) and at home (three sessions/week); and Intervention 2 was conducted at home only (five times/week). Interventions combined observation and imagination of missing limb movements. Participants rated their pain level and their ease to imagine daily throughout the study. RESULTS: Time series analyses showed that three participants rated their pain gradually and significantly lower during Intervention 1. During Intervention 2, additional changes in pain slopes were not significant. Four participants reported a reduction of pain greater than 30% from baseline to the end of Intervention 2, and only one maintained his gains after 6 months. Group analyses confirmed that average pain levels were lower after intervention than at baseline and had returned to baseline after 6 months. Social support, degree of functionality, and perception of control about their lives prior to the intervention correlated significantly with pain reduction. CONCLUSIONS: Persons with phantom limb pain may benefit from this novel intervention combining observation and motor imagery. Additional studies are needed to confirm our findings, elucidate mechanisms, and identify patients likely to respond.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".