Prevalence of Post-Traumatic Stress Disorder Following Caesarean Section: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: While caesarean section (CS) can be a lifesaving intervention when performed in a timely manner to overcome dystocia or other complications, it is a traumatic event and may increase the risk of post-traumatic stress disorder (PTSD). No attempt has been made to assess prevalence of PTSD after CS specifically. This study aimed to quantify pooled prevalence of PTSD after CS through a systematic review and meta-analysis. Methods: MEDLINE, PsycINFO, EMBASE, and CINAHL were searched using PTSD terms crossed with CS terms. Studies were included if they reported the prevalence of PTSD after CS using an instrument based on Diagnostic and Statistical Manual of Mental Disorders-criteria to identify PTSD. The pooled prevalence was then estimated by meta-analysis in overall eligible studies and in subgroups. Results: Nine studies were included with a total of 1,134 postpartum women, of which 136 were identified as having PTSD. Pooled prevalence of PTSD after CS was 10.7% (95% confidence interval [CI]: 4.0–20.2). Pooled prevalence of PTSD after emergency CS (10.3% [95% CI: 1.7–24.9]) was higher than that after elective CS (7.1% [95% CI: 0.7–19.4]), but the difference was not statistically significant. Subgroup analysis showed that pooled prevalence of PTSD after CS differed according to study setting, time interval of PTSD assessment, and type of participants. Meta-regression analysis showed that study setting and type of study participants were significant sources of heterogeneity. Conclusions: Women with CS apparently have higher rates of PTSD as compared with women without CS. However, the susceptibility to PTSD appears to vary based on emergency/elective CS, study methodology, self-perceived traumatic birth, and country of study. Further targeted research is needed to elucidate the role of these factors in relationship between CS and PTSD.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".