Effectiveness of muscle energy technique in patients with nonspecific low back pain: a systematic review with meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Low back pain (LBP) is a major cause of physical disability in the world. The origin of this condition can be due to differents causes, with a specific cause or of unknown mechanical origin,being characterized as unspecific. In this case a physical therapy treatment approach with manual therapy is relevant, which includes the muscle energy technique (MET) classified as a common conservative treatment for pathologies of the spine, mainly in LBP and disability. This study assessed the effectiveness of the muscle energy technique on nonspecific low back pain.EVIDENCE ACQUISITION: Patients with acute, subacute or chronic non-specific low back pain. The primary outcomes were pain and disability. This study was designed by a systematic review and meta-analysis, registered in PROSPERO (CRD42020219295). For the report and methodological definitions of this study, the recommendations of the PRISMA protocol and the Cochrane collaboration, were followed, respectively.EVIDENCE SYNTHESIS: The search yielded 164 citations, which 19 were eligible randomised trials were included in the review (N.=609 patients with low back pain). The methodological quality of the studies averaged 4.2 points, with an interval of 2 to 7 points. Three RCTs showed satisfactory methodological quality (PEDro Score ≥6). For patients with chronic LBP, a significant result on pain (but with a small and clinically unimportant effect) in favor of MET versus other (MD=-0.51 [95% CI,-0.93 to -0.09] P=0.02, N.=376, studies=11, I2=80%). In patients with subacute LBP, MET enabled a significant and moderate effect to reduce pain intensity when compared to the control group (MD=-1.32 [95% CI,-2.57 to -0.06] P=0.04, N.=120, studies=3, I2=88%). No significant effects were observed for the disability.CONCLUSIONS: MET is not considered an efficient treatment to improve the incapacity of the lumbar spine, but it may be beneficial in reducing the intensity of LBP, although showing a small clinical effect in chronic LBP and a moderate effect in subacute LBP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.004 | 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".