The effects of massage therapy on dysmenorrhea caused by endometriosis.
Bibliographic record
Abstract
BACKGROUND: Studying women's quality of life, we come across some harmful effects that factor such as dysmenorrhea caused by endometriosis leaves on their lives, their ability to work, their familial relations, and their self-confidence. Due to the repeated medical follow-ups and the side effects of medical therapies and endometriosis surgeries, many patients tend to use less expensive, nonmedical, and nonaggressive methods. The present study aimed to assess the effects of massage therapy, one of the aforementioned methods on endometriosis caused dysmenorrhea. METHODS: This was a semi-empirical clinical trial. Considering inclusion criteria, 23 patients suffering from endometriosis visited the Infertility Center of Isfahan, who were later confirmed by laparoscopy or laparotomy were picked as the sample through a simple method. The visual analog scale and McGill questionnaires were used once before and twice after the end of intervention for each patient. The data were analyzed using SPSS software. RESULTS: There was a statistically significant difference between the intensity of pain before the intervention started, immediately after, and also six weeks after it (p < 0.001). CONCLUSIONS: According to the results of this study and confirmations of other ones, it seems that massage therapy can be a fitting method to reduce the menstrual pain caused by endometriosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".