Potential effects of combining osteopathic manual therapy and menstrual awareness on pain and associated symptoms in women with primary dysmenorrhea: A randomized clinical trial
Bibliographic record
Abstract
Background Dysmenorrhea is a menstrual condition that accounts for 50–90 % of all gynaecological consultations and is the most common gynaecological condition among young women. Lack of information regarding treatment options can affect the symptoms and quality of life of women who suffer from it. Osteopathic manual therapy could be a treatment option to improve symptoms in primary dysmenorrhea. Objective The aim of this study was to apply an osteopathic manual therapy protocol to reduce menstrual pain and other symptoms related to primary dysmenorrhea. Methods A randomized clinical trial was conducted. Thirty-nine female volunteers diagnosed with primary dysmenorrhea, with a mean age of 30.4 years (SD = 5.67), were randomly assigned to two groups: an experimental group (n = 19) who received body awareness plus osteopathic manual therapy and a comparator group (n = 20) who received only body awareness. Pain intensity (Visual Analogue Scale), pain perception (the McGill Pain Questionnaire), quality of life (36-Item Short Form Survey Instrument), body satisfaction (Body Satisfaction and Global Self-Perception Questionnaire), and overall perception of change (Patient Global Impression of Change Scale) were assessed pre- and post-treatment. Results Comparing both groups, the experimental group showed a statistically significant improvement in pain intensity (p = 0.007), pain perception (p = 0.025), quality of life (p < 0.001), and body satisfaction (p < 0.001). In addition, most women in the experimental group (94.7 %) perceived a positive change after treatment, while most of the comparator group (65 %) reported no changes. Conclusion An osteopathic manual therapy protocol combined with body awareness revealed significant improvements in terms of pain and other symptoms in women with dysmenorrhea.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".