Acupuncture for Treating Persistent Pain in Brazilian Para-Athletes
Bibliographic record
Abstract
Background: Adapted or adaptive sports promote social inclusion for persons with impaired mobility. The practice of sports entails a risk of pain, which can lead to a decrease in physical performance and disruption of training. Analgesic treatment can aggravate the causative factor and exacerbate injury severity. In para-athletes, the preexisting clinical condition adds complexity to therapeutic protocols. Objectives: The aim of this study was to examine acupuncture use in para-athletes who had pain symptoms during training and to compare the results of two pain-assessment scales. Methods: Paralympic team members (N=7; 6 males and 1 female) were referred following failure of pharmacologic and physiotherapy pain treatments. Acupuncture was perfomed following the Traditional Chinese Medicine method for Bi syndrome, with systemic balance and local acupoints, for 30 minutes twice weekly for 6 weeks. Two pain-assessment scales (a visual analogue scale [VAS] and the McGill pain questionnaire) were adminstered at four timepoints: T1 (baseline); T2 (after four acupuncture therapy sessions); T3 (after eight sessions); and T4 (after 12 sessions). Results: Significant pain reduction was achieved. The para-athletes' pain scores were significantly different from baseline, starting at T3. One athlete who had no response to acupuncture and altered tongue characteristics died of liver cancer after this study. Conclusions: Pain symptoms were reduced with acupuncture. The VAS and McGill questionnaire pain-assessment results were concordant. The mean duration required for improvement was eight acupuncture sessions. Cases of no response to acupuncture associated with markedly altered tongue characteristics could be investigated in future studies as predictive indicators of morbid conditions in athletes.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".