Segmental stabilization and muscular strengthening in chronic low back pain - a comparative study
Bibliographic record
Abstract
OBJECTIVE: To contrast the efficacy of two exercise programs, segmental stabilization and strengthening of abdominal and trunk muscles, on pain, functional disability, and activation of the transversus abdominis muscle (TrA), in individuals with chronic low back pain. DESIGN: Our sample consisted of 30 individuals, randomly assigned to one of two treatment groups: segmental stabilization, where exercises focused on the TrA and lumbar multifidus muscles, and superficial strengthening, where exercises focused on the rectus abdominis, abdominus obliquus internus, abdominus obliquus externus, and erector spinae. Groups were examined to discovere whether the exercises created contrasts regarding pain (visual analogical scale and McGill pain questionnaire), functional disability (Oswestry disability questionnaire), and TrA muscle activation capacity (Pressure Biofeedback Unit = PBU). The program lasted 6 weeks, and 30-minute sessions occurred twice a week. Analysis of variance was used for inter- and intra-group comparisons. The significance level was established at 5%. RESULTS: As compared to baseline, both treatments were effective in relieving pain and improving disability (p < 0.001). Those in the segmental stabilization group had significant gains for all variables when compared to the ST group (p < 0.001), including TrA activation, where relative gains were 48.3% and -5.1%, respectively. CONCLUSION: Both techniques lessened pain and reduced disability. Segmental stabilization is superior to superficial strengthening for all variables. Superficial strengthening does not improve TrA activation capacity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".