Asian-white disparities in obstetric anal sphincter injury: Protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Obstetric anal sphincter injury (OASI) describes severe injury to the perineum and perineum and perianal muscles following birth and occurs in 4.4% to 6.0% of vaginal births in Canada. Studies from high-income countries have identified an increased risk of OASI in individuals who identify as Asian race versus those who identify as white. This protocol outlines a systematic review and meta-analysis which aims to determine the incidence of OASI in individuals living in high-income countries who identify as Asian versus those of white race/ethnicity. We hypothesize that the pooled incidence of OASI will be higher in Asian versus white birthing individuals. METHODS: We will search MEDLINE, OVID, Embase, Emcare and Cochrane databases from inception to 2022 for observational studies using keywords and controlled vocabulary terms related to race, ethnicity and OASI. Two reviewers will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and Meta-analysis of Observational Studies (MOOSE) recommendations. Meta-analysis will be performed using RevMan for dichotomous data using the random effects model and the odds ratio (OR) as effect measure with a 95% confidence interval (CI). Subgroup analysis will be performed based on Asian subgroups (e.g., South Asian, Filipino, Chinese, Japanese individuals). Study quality assessment will be performed using The Joanna Briggs Institute Critical Appraisal tools. DISCUSSION: The systematic review and meta-analysis that this protocol outlines will synthesize the extant literature to better estimate the rates of OASI in Asian and white populations in non-Asian, high-income settings and the relative risk of OASI between these two groups. This systematic summary of the evidence will inform the discrepancy in health outcomes experienced by Asian and white birthing individuals. If these findings suggest a disproportionate burden among Asians, they will be used to advocate for future studies to explore the causal mechanisms underlying this relationship, such as differential care provision, barriers to accessing care, and social and institutional racism. Ultimately, the findings of this review can be used to frame obstetric care guidelines and inform healthcare practices to ensure care that is equitable and accessible to diverse populations.
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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 | 0.059 | 0.110 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.077 | 0.007 |
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".