Comparing the Prevalence, Risk Factors, and Repercussions of Postpartum Genito-Pelvic Pain and Dyspareunia
Bibliographic record
Abstract
INTRODUCTION: Childbirth is a risk factor for developing genito-pelvic pain and/or dyspareunia during the postpartum period and potentially in the longer term. These two types of pain can occur simultaneously or sequentially and could be affected by different risk factors and have a range of repercussions to women's lives, including their sexual functioning. AIM: This study reviewed the available evidence to compare and contrast the prevalence, risk factors, and repercussions of postpartum genito-pelvic pain vs dyspareunia. METHODS: All available data related to postpartum genito-pelvic pain and dyspareunia were reviewed. MAIN OUTCOME MEASURES: A description of the prevalence, risk factors, and sexual and psychological consequences of postpartum genito-pelvic pain and dyspareunia and the methodologic limitations of previous studies. RESULTS: The prevalence of postpartum genito-pelvic pain is much lower than that of postpartum dyspareunia. There is evidence of converging and differential risk factors for acute and persistent experiences of these two types of pain. Postpartum genito-pelvic pain and dyspareunia are associated with impaired sexual functioning. Rarely are these pain experiences examined together to make direct comparisons. CONCLUSION: There has been a critical lack of studies examining postpartum genito-pelvic pain and dyspareunia together and integrating biomedical and psychosocial risk factors. This approach should be spearheaded by a multidisciplinary group of researchers of diverse and relevant expertise, including obstetricians, gynecologists, anesthesiologists, and psychologists.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".