The association between ethnicity and pre‐eclampsia in Australia: A multicentre retrospective cohort study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Rates of pre-eclampsia vary between countries and certain ethnic groups. However, there is limited evidence about the impact of ethnicity on risk of pre-eclampsia, beyond established clinical risk factors. AIMS: To assess the association between ethnicity and pre-eclampsia in Australia's diverse multi-ethnic population. MATERIALS AND METHODS: We conducted a retrospective cohort study using the ObstetriX database. We included all women with a birth between January 2011 and December 2014, at Auburn, Blacktown/Mount-Druitt and Westmead Hospitals in the Western Sydney Local Health District. We estimated the pre-eclampsia rate overall, and by maternal ethnic group, defined by country of birth and primary language. We developed multivariable logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (CIs) for pre-eclampsia, adjusting for maternal age, body mass index, autoimmune disease, chronic hypertension, chronic renal disease, diabetes mellitus (type 1 or 2), and multiple pregnancy. A secondary analysis was restricted to nulliparous women. RESULTS: There were 40 824 women evaluated, including 12 743 nulliparous women. Of these, 1448 (3.5%) developed pre-eclampsia (range: Australian/New Zealand-born English speakers 735/15 422 (4.8%); North-East Asian women 51/4470 (1.1%)). Relative to Australian/New Zealand-born English speakers, immigrants had a lower risk of pre-eclampsia overall (adjusted OR 0.67; 95% CI 0.60-0.75); as did the three largest immigrant groups examined: Southern Asian (0.73; 0.62-0.85), Middle-Eastern/African (0.55; 0.47-0.66) and North-East Asian (0.33; 0.25-0.45) women. Findings were similar for nulliparous women. CONCLUSIONS: Certain immigrant groups are at lower risk of pre-eclampsia than Australian/New Zealand-born English-speaking women. Understanding why this is so may lead to better screening and preventive strategies in higher-risk women.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it