Treating Primary Dysmenorrhoea with Acupuncture: A Narrative Review of the Relationship between Acupuncture ‘dose— and Menstrual Pain Outcomes
Bibliographic record
Abstract
OBJECTIVE: A number of randomised controlled trials have been performed to determine the effectiveness or efficacy of acupuncture in primary dysmenorrhoea. The objective of this review was to explore the relationship between the 'dose' of the acupuncture intervention and menstrual pain outcomes. METHODS: Eight databases were systematically searched for trials examining penetrating body acupuncture for primary dysmenorrhoea published in English up to September 2015. Dose components for each trial were extracted, assessed by the two authors and categorised by neurophysiological dose (number of needles, retention time and mode of stimulation), cumulative dose (total number and frequency of treatments), needle location and treatment timing. RESULTS: Eleven trials were included. Components of acupuncture dose were well reported across all trials. The relationship between needle location and menstrual pain demonstrated conflicting results. Treatment before the menses appeared to produce greater reductions in pain than treatment starting at the onset of menses. A single needle during menses may provide greater pain reduction compared to multiple needles. Conversely, multiple needles before menses were superior to a single needle. Electroacupuncture may provide more rapid pain reduction compared to manual acupuncture but may not have a significantly different effect on overall menstrual pain. CONCLUSIONS: There appear to be relationships between treatment timing and mode of needle stimulation, and menstrual pain outcomes. Needle location, number of needles used and frequency of treatment show clear dose-response relationships with menstrual pain outcomes. Current research is insufficient to make definitive clinical recommendations regarding optimum dose parameters for treating primary dysmenorrhoea.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".