Impact of gestational diabetes mellitus on women’s sexual function: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication with well-established adverse effects on maternal and fetal health. However, research on its impact on sexual health is inconsistent. Currently, there is no comprehensive review on sexual function in pregnant women with GDM. The purpose of this study is to systematically gather and synthesize the available evidence, addressing this important research gap. METHODS: This systematic review and meta-analysis utilized a comprehensive literature search strategy and incorporated the following databases: the Cochrane Library, Scopus, PubMed, Web of Science, SID, and Google Scholar. The search was conducted until February 21, 2024. The quality of the cross-sectional and case‒control studies included in the current study was evaluated via the modified and standard Newcastle‒Ottawa scale. The certainty of the evidence was evaluated via the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework. A meta-regression was conducted to examine the variables that influence total sexual function. Additionally, sequential analysis was performed to determine the required information size for the meta-analysis. RESULTS: The systematic search process yielded a total of 370 studies. The final analysis included six studies. The meta-analysis findings revealed that compared with controls, women with GDM had significantly lower total scores for sexual function (SMD - 1.80, 95% CI -3.44 to -0.15, p = 0.03), sexual desire (SMD - 5.14, 95% CI -8.14 to -2.14, p < 0.001), arousal (SMD - 0.58, 95% CI -0.95 to -0.21, p = 0.002), lubrication (MD -0.41, 95% CI -0.59 to -0.22, p < 0.001) and satisfaction (SMD - 3.82, 95% CI -6.08 to -1.57, p < 0.001). However, the analysis did not reveal statistically significant differences in sexual pain, or orgasm between the GDM and control groups. The meta-regression analysis revealed that older age in the control group was associated with poorer sexual function. CONCLUSION: Compared with control women, pregnant women diagnosed with GDM have lower sexual function. Further research with larger sample sizes is necessary to enhance the robustness of the evidence, given the low level of certainty. Healthcare providers should focus on the sexual well-being of women with GDM and create tailored interventions to address their specific needs.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".