The effects of a novel pilates exercise prescription method on people with non-specific unilateral musculoskeletal pain: a randomised pilot trial
Bibliographic record
Abstract
Background: Pain alters the neuromuscular activation and results in altered movement adaptations. A new exercise prescription method proposes that we can restore the neuromuscular control by rehabilitating the deficient neural drive through Pilates exercises. This is done by identifying the postural control deficits using single-leg tests such as hopping, half squats and heel raises. The aim of this study was to find out if this method of prescribing exercises results in clinically relevant outcomes. Methods: Fifteen patients with chronic non-specific low back pain with unilateral musculoskeletal pain were recruited. Following consent, all patients were randomly assigned either to perform gym or Pilates-based individualised exercises once weekly for six weeks. The primary outcome was to measure the pain intensity using a 0–10 numerical rating scale. The secondary outcome measures were: the global perceived effect scale (GPE; 0–10), the patient-specific functional scale (PSFS; the patient-generated measure of disability) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC; the condition-specific measure of disability). Results: There were statistically significant differences noted after intervention within the control group in the numerical rating scale ( P=0.041), GPE ( P=0.024), PSFS ( P=0.039) and within the experimental group in the WOMAC ( P=0.008), GPE ( P=0.007) and PSFS ( P=0.007). However, as there were clinically significant baseline differences, the within-group difference could be due to regression to the mean. There were no statistically significant results between the two groups after intervention. Conclusion: This new prescription method for Pilates-based exercises may improve disability and global perception of recovery. However, the outcomes are not different from a regular gym-based exercise programme.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".