Disease Burden of Dysmenorrhea: Impact on Life Course Potential
Bibliographic record
Abstract
Brittany MacGregor,1 Catherine Allaire,1 Mohamed A Bedaiwy,1 Paul J Yong,1,* Olga Bougie2,* 1Department of Obstetrics and Gynaecology, University of British Columbia, BC Women’s Centre for Pelvic Pain and Endometriosis, Vancouver, Canada; 2Department of Obstetrics and Gynaecology, Queen’s University, Kingston, Canada*These authors contributed equally to this workCorrespondence: Olga Bougie, 76 Stuart Street, Victory 4, Department of Obstetrics & Gynecology, Queen’s University, Kingston, Ontario, Canada, K7L 2V7, Email Ob17@queensu.ca Paul J Yong, Department of Obstetrics & Gynecology, University of British Columbia, FRCSC, F2 – 4500 Oak Street, Vancouver, British Columbia, V6H3N1, Canada, Email Paul.Yong@vch.caAbstract: Dysmenorrhea is the most common gynecologic condition among the female population and has a significant impact on life course potential. It has a widespread impact on a female’s mental and physical well-being, with longstanding impairments on quality of life, personal relationships, and education and career attainment. Furthermore, untreated dysmenorrhea can lead to hyperalgesic priming, which predisposes to chronic pelvic pain. Primary dysmenorrhea is pain in the lower abdomen that occurs before or during menses and in the absence of pelvic pathology. One possible mechanism is endometrial inflammation and increased prostaglandin release, resulting in painful uterine contractions. Dysmenorrhea may also occur secondary to pelvic pathology, such as endometriosis, adenomyosis or due to cyclic exacerbation of non-gynecologic pain conditions. A thorough patient evaluation is essential to differentiate between potential causes and guide management. Treatment must be tailored to individual patient symptoms. Pharmacologic management with non-steroidal anti-inflammatory medications and/or combined hormonal contraceptives is most common. Heat therapy, exercise, vitamins and dietary supplements have limited evidence and can be offered for patients seeking non-pharmacologic adjunctive or alternative options. Greater awareness for both health-care providers and patients allows for early intervention to reduce impact on quality of life and life course potential.Keywords: primary dysmenorrhea, adolescents, chronic pain, health trajectory, women’s health
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".