Effects of a Myofascial Technique on the Stiffness and Thickness of the Thoracolumbar Fascia and Lumbar Erector Spinae Muscles in Adults with Chronic Low Back Pain: A Randomized before-and-after Experimental Study
Bibliographic record
Abstract
The thoracolumbar fascia (TLF) may be a pain generator, given its rich innervation. Structural and biomechanical changes have also been documented in adults with chronic non-specific low back pain (LBP). Myofascial techniques (MFTs) are commonly used in manual therapy and are hypothesized to reduce tissue stiffness and pain. However, evidence for these effects is limited. The objective of this study was to evaluate the immediate effects of a standardized MFT compared to a simulated MFT on: (1) the stiffness of the TLF and erector spinae muscles (shear-wave sonoelastography), (2) the thickness of the TLF (B-mode ultrasound), and (3) pain intensity (numerical rating scale). Forty-nine participants with chronic non-specific LBP were included in a randomized before-and-after experimental study. Outcome measures were collected before (T0) and immediately after the intervention (T1). Pain intensity was also assessed on day two (T2) and seven (T7). The MFT group showed a significant decrease in left erector spinae muscle stiffness and left TLF thickness compared to the simulated group. In addition, there was a significant reduction in pain intensity in the MFT group compared to the simulated group at T1 and T2. The results of this study suggest that MFT results in immediate tissue changes and transient pain reduction in patients with 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".