The effect of spinal manipulative therapy and ischaemic compression versus muscle energy technique in chronic nonspecific neck pain
Bibliographic record
Abstract
Neck pain has become a problem experienced worldwide and it poses a global healthcare challenge to practising medical professions. There are numerous manual and non-manual treatments available for this frequently encountered problem. Frequently utilised and effective therapies are spinal manipulative therapy (SMT) and ischaemic compression (IC); however, these have been associated with several contraindications. An alternative form of treatment with less contraindications that may be of benefit to the patient is muscle energy technique (MET). Therefore, the aim of this study was to determine the effect of spinal manipulative therapy and ischaemic compression compared to muscle energy technique in chronic nonspecific neck pain. Methodology: This study was a quantitative randomised, single blinded clinical trial. Forty participants with nonspecific pain, aged 20-50 years, were randomly allocated into two groups using a random allocation chart provided by a statistician. Group one received SMT and IC, whereas group two received MET alone. The numerical pain rating scale (NRS) was used to determine the level of neck pain. The cervical range of motion (CROM) goniometer was used to calculate the degree of lateral flexion occurring at the neck. The pain pressure algometer was used to determine the pain pressure thresholds (PPT). The Canadian Memorial Chiropractic College (CMCC) Neck Disability Index (NDI) was used to assess the disability in activities of daily living as a consequence of neck pain. Each participant had four consultations over a two-week period, receiving treatment on the first three consultations with the fourth being purely subjective and objective measurements. Results: Repeated measures ANOVA testing was utilised to examine the changes over time in each group. Profile plots were used to visually explore the trends of each group over time. Intra-group analysis of subjective and objective measurements revealed that both groups had a beneficial response to the treatment over time. Inter-group analysis showed that there were no statistically significant differences between the two groups in terms of subjective and objective measurements. Conclusion: In conclusion, this study revealed that the use of MET is as equally effective as a combination of SMT and IC in the treatment of chronic nonspecific neck 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".