EFFICACY OF ONE TIME TRANSCUTANEOUS ELECTRICAL NEUROMUSCULAR STIMULATION (TENS) IN NEXT TWO MENSTRUAL CYCLES IN PRIMARY DYSMENORRHEA
Bibliographic record
Abstract
Background: Dysmenorrhea refers to the occurrence of painful menstrual cramps of uterine origin.TENS may be an alternative treatment option for women with dysmenorrhea who wish to stop using non-steroidal antiinflammatory drugs (NSAIDs), oral contraceptives, or other analgesics because the existing medication is ineffective, has unacceptable adverse effects, or due to personal choice.An effective non-pharmacological method of treating dysmenorrhea would be of great potential value in treating dysmenorrhea.Materials and Methods: 50 females with age group of 20-30 years having moderate or severe degree of disability due to dysmenorrhea with VAS score > 7 without athletic background were taken into the study.Conventional TENS was applied on 1 st and 2 nd day of menses over the abdomen in criss-cross pattern and effect of TENS was evaluated in present and next two menstrual cycles.Assessment tools were Visual analogue scale (VAS), Shortform McGill Pain Questionnaire(SF-MPQ) and Short-form Moos Menstrual Distress Questionnaire (SF-MMDQ) and Outcome measures were Pain ( assessed by VAS and SF-MPQ), Quality of life (assessed by SF-MMDQ) and Number of analgesics used.Results: The present study demonstrates that there is decrease in pain and improvement in quality of life after application of TENS in present cycle as well as in next two menstrual cycles in primary dysmenorrhea as evident by decrease in VAS (mean) score, SF-MPQ (mean) score and SF-MMDQ (mean) score(p=0.05).Conclusion: Conventional TENS is effective in relieving pain and improving quality of life in moderate degree of disability due to primary dysmenorrhea.Majority of subjects have shown relief of pain and improvement in quality of life in next 2 menstrual cycles and didn't need any analgesic after one time TENS treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".