Effectiveness of spinal manipulation in chronic mechanical low back pain: a gender-based comparative study
Bibliographic record
Abstract
Background. The purpose of this study was to compare and evaluate the effects of spinal manipulation in patients with chronic mechanical low back pain with respect to gender. Methods. A total of 62 volunteers diagnosed with chronic mechanical low back pain were included in the study. Participants were divided into two groups based on gender: female (n = 31) and male (n = 31). Functional disability was assessed using the Oswestry Low Back Pain Disability Questionnaire (OLBPDQ). Pain intensity and characteristics were evaluated using the Visual Analog Scale (VAS) and the McGill Pain Questionnaire. Quality of life was assessed with the WHOQOL-Bref, sleep quality with the Pittsburgh Sleep Quality Index (PSQI), and treatment satisfaction with the Treatment Satisfaction Scale (TSS). All assessments were conducted at three time points: before treatment, immediately after treatment, and one month following treatment completion. Goniometric measurements of the hip and lumbar spine were also performed. Both groups received identical interventions consisting of spinal manipulation applied twice weekly, for a total of eight sessions over four weeks. Results. Statistically significant within-group improvements were observed across all outcome measures at post-treatment and at the one-month follow-up compared with baseline, with the exception of the Treatment Satisfaction Scale (p < 0.05). When outcomes were compared between female and male groups, no statistically significant differences were identified (p > 0.05). Conclusion. Gender does not influence the effectiveness of spinal manipulation in the management of chronic mechanical 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 | 0.001 | 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".