Predictors for sexual dysfunction in the first year postpartum: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Pregnancy and childbirth increase the risk for pelvic floor dysfunction, including sexual dysfunction. So far, the mechanisms and the extent to which certain risk factors play a role, remain unclear. Objectives In this systematic review of literature, we aimed to determine the risk factors for sexual dysfunction in the first year after delivery. Search Strategy We searched MEDLINE, Embase and CENTRAL using the search strategy: Sexual dysfunction AND Obstetric events. Selection Criteria We included original English, comparative studies that used validated questionnaires and the ICS/IUGA terminology for sexual dysfunction, dyspareunia and vaginal dryness. Data Collection and Analysis We assessed the quality and the risk of bias of the included studies with the Newcastle Ottawa Scale. We extracted the reported data and we performed random-effects meta-analysis to obtain the summary Odds Ratios (OR) with 95% Confidence Intervals. Heterogeneity across studies was assessed using the I2 statistic. Main Results We found no significant difference in the odds for both sexual dysfunction and dyspareunia between cesarean section and spontaneous delivery (OR:1.17[0.88-1.57] and OR:0.75[0.53-1.07]) and between operative delivery and spontaneous delivery (OR:1.56[0.87-2.79] and OR:1.35[0.75-2.42]). Anal sphincter injury was associated with increased odds for both sexual dysfunction (OR:3.00[1.28-7.03]) and dyspareunia (OR:1.71[1.09-2.67]). Episiotomy was associated with dyspareunia (OR:1.65[1.20-2.29]) but not with sexual dysfunction (OR:1.90[0.94-3.84]). We retrieved one study of low quality which reported on vaginal dryness and found no significant association with obstetric events.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 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".