Comparison of adverse perinatal outcomes between Asians and Caucasians: a population-based retrospective cohort study in Ontario
Bibliographic record
Abstract
BACKGROUND: Racial disparities in adverse perinatal outcomes have been studied in other countries, but little has been done for the Canadian population. In this study, we sought to examine the disparities in adverse perinatal outcomes between Asians and Caucasians in Ontario, Canada. METHODS: We conducted a population-based retrospective cohort study that included all Asian and Caucasian women who attended a prenatal screening and resulted in a singleton birth in an Ontario hospital (April 1st, 2015-March 31st, 2017). Generalized estimating equation models were used to estimate the independent adjusted relative risks and adjusted risk difference of adverse perinatal outcomes for Asians compared with Caucasians. RESULTS: percentile), neonatal intensive care unit admission, and hyperbilirubinemia requiring treatment, but had lower risks of preeclampsia, macrosomia (birth weight > 4000 g), large-for-gestational-age neonates, 5-min Apgar score < 7, and arterial cord pH ≤7.1, as compared with Caucasians. No difference in risk of elective cesarean section was observed between Asians and Caucasians. CONCLUSION: There are significant differences in several adverse perinatal outcomes between Asians and Caucasians. These differences should be taken into consideration for clinical practices due to the large Asian population in Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".