Seeking the root causes of menstrual pain: A systematic review of biomarkers in menstrual effluent
Bibliographic record
Abstract
Dysmenorrhea (period pain) affects over 40% of women and is a leading cause of missed school and workdays. However, the molecular mechanisms underlying this pain are not fully understood. We conducted a systematic review (Prospero registration: CRD42024535081) to identify and evaluate the biomolecules in menstrual effluent that may contribute to dysmenorrhea and assess how non-hormonal medications (e.g. NSAIDs) impact these biomarkers. Fifteen studies involving two hundred twenty-three participants met the inclusion criteria. We used the Newcastle-Ottawa Scale (for observational studies) and the Cochrane RoB2 tool (for randomized controlled trials) to evaluate the risk of bias and the quality of studies. Eight studies consistently reported elevated prostaglandin levels in the menstrual effluent of women with dysmenorrhea, though sample sizes were generally small, and methodological issues were noted. Seven studies demonstrated that NSAIDs reduce prostaglandin concentrations; however, these trials utilized multiple-day dosing protocols instead of single-dose regimens, leaving questions about acute treatment effects. Two studies highlighted alternative molecular targets, such as 12-HETE and platelet-activating factor (PAF), that may also play key roles in menstrual pain. Overall, elevated prostaglandins are a recurring finding, but the limited scope and design of existing studies indicate a need for larger, methodologically rigorous investigations. Nevertheless, the few studies that identified molecules other than prostaglandins suggest there are viable druggable targets for clinical trials to reduce menstrual pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".