The clinical and biomechanical effects of fascial-muscular lengthening therapy on tight hip flexor patients with and without low back pain.
Bibliographic record
Abstract
BACKGROUND: Many patients have tight hip flexors with or without low back pain. Manual fascial-muscular lengthening therapy (FMLT) is one commonly used treatment for this population. OBJECTIVE: Investigate the clinical and biomechanical effects of manual FMLT on tight hip flexor patients with and without low back pain. METHODS: A nonrandomized trial, before-and-after experiment with multiple baselines conducted on two different patient populations: 1) Mechanical low back pain patients with tight hip flexors (n = 10) and 2) Asymptomatic group with tight hip flexors (n = 8). Four treatments of manual FMLT were performed on the hip flexor of the two groups of patients over a two-week period. Primary outcome measures over the two-week period were 1) Maximum voluntary trunk flexor and extensor moments, 2) Disability (Roland Morris Disability Questionnaire) and pain (10-cm Visual Analogue Scale), 3) Passive hip extension mobility. RESULTS: Primary outcome analysis involved within-groups comparisons. Maximum voluntary trunk extension demonstrated increases for the low back pain patients. The low back pain patients demonstrated a small, but significant, reduction in disability and pain. Both groups demonstrated an increase in passive hip extension measurements. CONCLUSION: This preliminary study demonstrated interesting results from manual FMLT on two tight hip flexor patient populations with and without low back pain. However, there were several significant limitations from this study, which restrict the ability to generalize the results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".