influence of virtual reality approach on phantom pain in trans-tibial amputation
Bibliographic record
Abstract
Background: Phantom limb pain (PLP): is a common complaint after lower limb amputation can be defined as discomfort or pain in a missing part of the limb. This study was conducted in the research laboratory section, Faculty of Physical Therapy, Cairo University, Giza, Egypt. Purpose: To investigate the effect of Virtual reality (VR) on phantom limb pain and lower limb Function in trans tibial amputation. Methods: sixty patient with phantom pain were enrolled into 2 equal groups: a study group and a control group. Outcome measures included pain intensity level was measured by McGill pain questionnaire and Lower Extremities Function was measured by Lower Extremities Function Scale. That were assessed at baseline and 4 weeks postintervention. Results: A statistically significant effect (p < 0.0001) of treatment and time was revealed in both groups for all measured variables. Between-group analysis implied a higher improvement in post-intervention results in group B (p < 0.05). Conclusion: This study indicated that Using VR with conventional treatment is more effective in case of Phantom pain in trans-tibial amputation that in improving pain intensity level (reduce pain) and Lower Extremities Function than conventional treatment alone.
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 | 0.000 | 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".