Effectiveness of yoga intervention enhanced by progressive muscularrelaxation on pain in women after breast cancer surgery
Bibliographic record
Abstract
Introduction The aim of the study was to evaluate the effectiveness of yoga intervention enhanced by progressive muscular relaxation on pain in women after breast cancer surgery Methods Overall, 84 patients after Madden mastectomy were eligible for this study. After the exclusion of 7 women, 77 participants were randomly allocated to group A (n = 38), receiving progressive muscular relaxation and visualization exercises in addition to yoga intervention, and group B (n = 39), receiving yoga intervention only. McGill Pain Questionnaire and visual analogue scale were used to evaluate pain at baseline and after the 4-week intervention. Results Most of the investigated pain characteristics in both studied groups steadily improved during the 4-week rehabilitation. However, the 4-week monitoring indicated that using progressive muscular relaxation and visualization exercises in addition to the yoga intervention was more effective for reducing self-reported pain in women after Madden mastectomy. The post-intervention level of pain reported in the visual analogue scale and present pain intensity were statistically lower in group A compared with group B by 0.99 points (p < 0.05) and 1.68 points (p < 0.05), respectively. Conclusions Performing progressive muscular relaxation and visualization exercises in addition to yoga intervention helped reduce pain in women after Madden mastectomy.
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) | 0.001 | 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) | 0.002 | 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".