Effectiveness of Pilates exercise on low back pain: a systematic review with meta-analysis
Bibliographic record
Abstract
PURPOSE: Low back pain is the most frequently reported musculoskeletal disorder and represents one of the highest patient burdens in healthcare. This systematic review and meta-analysis aimed to investigate the effectiveness of Pilates exercise on pain intensity and functional disability caused by low back pain (LBP). MATERIALS AND METHODS: A Systematic review with meta-analysis was conducted. Data sources: MEDLINE-NLM and MEDLINE-EBSCO. We also searched on Scopus Elsevier, Cochrane, DOAJ, SciELO, PEDro, and PLOS ONE databases. Eligibility criteria: randomized controlled trials (RCTs) evaluating LBP in which the primary treatment was based on Pilates exercise compared with no exercise, or non-specific exercise. RESULTS: The search returned 1566 records of which 36 articles were included in the systematic review and 19 in the meta-analysis. Twenty-two studies compared the effects of Pilates exercise vs no exercise and 13 studies examined the effects of Pilates exercise vs non-specific exercise. Analysis showed that Pilates had a positive effect on the perception of LBP vs no exercise. A similar trend occurred with non-specific exercise. CONCLUSIONS: Pilates exercise can decrease LBP compared to no exercise and non-specific exercise. General practitioners should consider Pilates exercise as an effective strategy to manage LBP and counteract the growing health. TRIAL REGISTRATION: PROSPERO registration number: CRD42022308387.IMPLICATIONS FOR REHABILITATIONPilates is a good strategy for improving low back pain and is more effective than other exercise programs or no exercise.Pilates is a safe tool to apply to most of the population with low back pain.Pilates is a non-pharmacological strategy useful for counteracting low back pain.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.054 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".