Non-invasive Complementary Therapies in Managing Musculoskeletal Pains and in Preventing Surgery
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal disorders are disabling diseases which affect work performance, thereby affecting the quality of life of individuals. Pharmacological and surgical management are the most recommended treatments. However, non-invasive physical therapies are said to be effective, for which the evidence is limited. AIM/PURPOSE: To study the effect of non-invasive physical interventions in preventing surgery among patients recommended for surgery for musculoskeletal complaints, who attended sports and fitness medicine centres in India. SETTINGS: SPARRC (Sports Performance Assessment Research Rehabilitation Counselling) Institute) is a physical therapy centre with 13 branches spread all over India. This Institute practices a combination of manual therapies to treat musculoskeletal complaints. RESEARCH DESIGN: Descriptive cohort study involving the review of case records of the patients enrolled from June 2013 to July 2017, followed by the telephone survey of the patients who have completed treatment. INTERVENTION: Combination of physical therapies such as myofascial trigger release with icing, infra-red therapy, pulsed electromagnetic field therapy, stretch release, aqua therapy, taping, and acupuncture were employed to reduce the pain and regain functionalities. MAIN OUTCOME MEASURES: Self-reported pains were measured using visual analogue scale at different levels of therapy-preand post-therapy and post-rehabilitation. RESULTS: value = .00). Among those contacted post-rehabilitation, 82 patients remained without surgery, and the median surgery-free time was around two years. CONCLUSION: Thus the study concluded that non-invasive physical therapies may prevent or postpone surgeries for musculoskeletal complaints.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".