Comparison of motor imagery focused pelvic floor exercises and relaxation exercises for treating dysmenorrhea: A randomized controlled study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate the impact of integrating motor imagery-focused pelvic floor exercises (MOPEXE) and relaxation exercise (RE) on pain and menstrual symptoms in the management of dysmenorrhea. MATERIALS AND METHODS: 60 participants with dysmenorrhea were randomly allocated to MOPEXE, RE, and a combination group that included both exercises. Participants were instructed to perform the relevant exercise program 3-d per week for eight weeks. The Menstruation Attitude Questionnaire (MAQ), Functional and Emotional Dysmenorrhea Scale (FEDS), and Short-Form McGill Pain Questionnaire (SF-MPQ) were used to assess pre- and post-treatment participant-reported outcomes. RESULTS: Combination therapy revealed a significant decrease in VAS and FEDS (emotional) and an increase in MAQ (deliberating event, natural event, anticipation, and denial parameters) compared to MOPEXE. MOPEXE group demonstrated a significant increase in menstruation as a bothersome event and a decrease in FEDS (functional). In comparison with the RE group, MAQ scores were significantly higher in the combination group. A significant reduction in VAS and an increase in the denial parameter of MAQ were observed in the RE group. CONCLUSION: Physical therapy with pelvic floor and relaxation exercises centered on motor imagery is an effective measure to reduce painful symptoms and to relieve dysmenorrhea both in terms of functional and emotional aspects.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 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 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".