The incidence of post-traumatic stress disorder (PTSD) following traumatic childbirth: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Birth trauma may be a risk factor for postpartum post-traumatic stress disorder (PTSD). However, no systematic review on postpartum PTSD in women with traumatic childbirth has been reported. Objective This study aimed to estimate the incidence of PTSD following traumatic childbirth through systematic review and meta-analysis. Search strategy Six databases (CINAHL, PsycINFO, Embase, PubMed, CNKI and Wanfang) were searched from inception to 28 February 2022. Selection criteria Cohort studies and cross-sectional studies related to the incidence of PTSD following traumatic childbirth were included. Data collection and analysis Two reviewers independently conducted studies selection, quality evaluation of studies, and data extraction. The Random-effects meta-analysis was conducted to derive the pooled incidence using Stata 16.0 software. Main results A total of nine studies with 1,823 women experienced traumatic birth were included. Of them, 353 were identified as PTSD. The pooled incidence of PTSD after traumatic childbirth was 19.2% (95%CI: 11.9%~26.5%). Subgroup analyses showed that the incidence of PTSD varied with traumatic birth/PTSD assessment scales and time, and type of study participants. Meta-regression analyses indicated that the study country was a significant source of heterogeneity, and the sample size birth trauma/PTSD were potential predictors of incidence of PTSD after a traumatic birth. Sensitivity analysis by deleting one study at a time yielded similar results. Conclusions The incidence of PTSD in women with traumatic childbirth is about 19%, which is much higher than that in general postpartum population. Keywords post-traumatic stress disorder, incidence, traumatic childbirth, postpartum, meta-analysis
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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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".