Birth-Related Perineal Trauma in Low- and Middle-Income Countries: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction Birth-related perineal trauma (BPT) is a common consequence of vaginal births. When poorly managed, BPT can result in increased morbidity and mortality due to infections, haemorrhage, and incontinence. This review aims to collect data on rates of BPT in low- and middle-income countries (LMICs), through a systematic review and meta-analysis. Methods The following databases were searched: Medline, Embase, Latin American and Caribbean Health Sciences Literature (LILACs), and the World Health Organization (WHO) regional databases, from 2004 to 2016. Cross-sectional data on the proportion of vaginal births that resulted in episiotomy, second degree tears or obstetric anal sphincter injuries (OASI) were extracted from studies carried out in LMICs by two independent reviewers. Estimates were meta-analysed using a random effects model; results were presented by type of BPT, parity, and mode of birth. Results Of the 1182 citations reviewed, 74 studies providing data on 334,054 births in 41 countries were included. Five studies reported outcomes of births in the community. In LMICs, the overall rates of BPT were 46% (95% CI 36-55%), 24% (95% CI 17-32%), and 1.4% (95% CI 1.2-1.7%) for episiotomies, second degree tears, and OASI, respectively. Studies were highly heterogeneous with respect to study design and population. The overall reporting quality was inadequate. Discussion Compared to high-income settings, episiotomy rates are high in LMIC medical facilities. There is an urgent need to improve reporting of BPT in LMICs particularly with regards to births taking in community settings.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.009 | 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) | 0.004 | 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".