Effects of Muscular Stretching and Segmental Stabilization on Functional Disability and Pain in Patients with Chronic Low Back Pain: A Randomized, Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to compare the effects of 2 exercise programs, segmental stabilization exercises (SSEs) and stretching of trunk and hamstrings muscles, on functional disability, pain, and activation of the transversus abdominis muscle (TrA), in individuals with chronic low back pain. METHODS: A total of 30 participants were enrolled in this study and randomly assigned to 1 of 2 groups as a function of intervention. In the segmental stabilization group (SS), exercises focused on the TrA and lumbar multifidus muscles, whereas in the stretching group (ST), exercises focused on stretching the erector spinae, hamstrings, and triceps surae. Severity of pain (visual analog scale and McGill pain questionnaire) and functional disability (Oswestry disability questionnaire) and TrA muscle activation capacity (Pressure Biofeedback Unit, or PBU) were compared as a function of intervention. Interventions lasted 6 weeks, and sessions happened twice a week (30 minutes each). Analysis of variance was used for intergroup and intragroup comparisons. RESULTS: As compared with baseline, both treatments were effective in relieving pain and improving disability (P < .001). Those in the SS group had significantly higher gains for all variables. The stretching group did not effectively activate the TrA (P = .94). CONCLUSION: Both techniques improved pain and reduced disability. In this study, SS was superior to muscular stretching for the measured variables associated with chronic low back 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".