Multimodal Therapy Combining Spinal Manipulation, Transcutaneous Electrical Nerve Stimulation, and Heat for Primary Dysmenorrhea: A Prospective Case Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this case study was to report the effects of multimodal therapy as an adjunct to oral contraceptives on pain and menstrual symptoms in a patient with primary dysmenorrhea. CLINICAL FEATURES: A 27-year old nulligravid and nulliparous woman presented with low back pain, thigh pain, and menstrual symptoms associated with primary dysmenorrhea. Multimodal therapies (spinal manipulation, clinic-based transcutaneous electrical nerve stimulation, and heat applied at home) were delivered over 3 menstrual cycles. Outcome measures included pain (visual analogue scale) and menstrual symptoms (Menstrual Distress Questionnaire) from baseline to follow-up. She continued to take her oral contraceptives throughout the study period. INTERVENTION AND OUTCOME: For both low back and thigh pain, the patient reported clinically important differences in average pain and worst pain after 2 and 3 months from baseline. There were no clinically important differences in current pain, best pain, or menstrual symptoms at follow-up. No adverse events were reported. CONCLUSION: Some of this patient's dysmenorrhea symptoms responded favorably to multimodal therapy over 5 months. The authors observed important short-term reductions in low back and thigh pain (average and worst pain intensity) during care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".