Effectiveness of craniosacral therapy in the human suboccipital region on hamstring muscle: A meta-analysis based on current evidence
Bibliographic record
Abstract
BACKGROUND: Craniosacral therapy (CST) has remained controversial in the treatment of musculoskeletal disorders. To our knowledge, there is no larger sample size of research to demonstrate the effectiveness of craniosacral therapy in the human suboccipital region on hamstring muscle. METHODS: To study whether the CST in the human suboccipital region could have a remote effect on the flexibility of the hamstring muscles, the Cochrane Library, Medline/Pubmed, CNKI, Embase, and Google Scholar were searched. Clinical trials assessing the effects of CST in short hamstring syndrome patients were eligible. Mean differences (MD) and 95% confidence intervals (CI) were calculated for the straight leg raise test (primary outcomes). The quality of the included studies was assessed using the Newcastle-Ottawa Scale. RevMan 5.3 software was used for data analysis. RESULTS: Five controlled trials with a total of 238 participants were included. CST could effectively relieve the symptoms of short hamstring syndrome patients [the overall MD -9.47, 95% confidence interval (CI) -15.82 to -3.12, P < .000001]. The CST was better than the proprioceptive neuromuscular facilitation technique (MD 3.09, 95% CI 1.48-4.70, P = .0002). Sensitivity analysis shows that the frequency of treatment and who did the experiment might be the main sources of impact results. CONCLUSION: CST could change the flexibility of the hamstring muscles. CST had a better curative effect when compared to proprioceptive neuromuscular facilitation technique on the hamstring muscles.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.049 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".