A comparison of the effects of pilates and mckenzie training on pain and general health in men with chronic low back pain: A randomized trial
Bibliographic record
Abstract
BACKGROUND: Today, chronic low back pain is one of the special challenges in healthcare. There is no unique approach to treat chronic low back pain. A variety of methods are used for the treatment of low back pain, but the effects of these methods have not yet been investigated adequately. AIM: The aim of this study was to compare the effects of Pilates and McKenzie training on pain and general health of men with chronic low back pain. MATERIALS AND METHODS: Thirty-six patients with chronic low back pain were chosen voluntarily and assigned to three groups of 12 each: McKenzie group, Pilates group, and control group. The Pilates group participated in 1-h exercise sessions, three sessions a week for 6 weeks. McKenzie group performed workouts 1 h a day for 20 days. The control group underwent no treatment. The general health of all participants was measured by the General Health Questionnaire 28 and pain by the McGill Pain Questionnaire. RESULTS: = 0.327). Neither of the two methods was superior over the other for pain relief. However, there was a significant difference in general health indexes between Pilates and McKenzie groups. CONCLUSION: Pilates and McKenzie training reduced pain in patients with chronic low back pain, but the Pilates training was more effective to improve general health.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".