Maternal Origin and Risk of Neonatal and Maternal ICU Admission*
Bibliographic record
Abstract
OBJECTIVES: To evaluate maternal world region of birth, as well as maternal country of origin, and the associated risk of admission of 1) a mother to a maternal ICU, 2) her infant to a neonatal ICU, or 3) both concurrently to an ICU. DESIGN: Retrospective population-based cohort study. SETTING: Entire province of Ontario, Canada, from 2003 to 2012. PATIENTS: All singleton maternal-child pairs who delivered in any Ontario hospital. MEASUREMENTS AND MAIN RESULTS: We explored how maternal world region of birth, and specifically, maternal country of birth for the top 25 countries, was associated with the outcome of 1) neonatal ICU, 2) maternal ICU, and 3) both mother and newborn concurrently admitted to ICU. Relative risks were adjusted for maternal age, parity, income quintile, chronic hypertension, diabetes mellitus, obesity, dyslipidemia, drug dependence or tobacco use, and renal disease. Compared with infants of Canadian-born mothers (110.7/1,000), the rate of neonatal ICU admission was higher in immigrants from South Asia (155.2/1,000), Africa (140.4/1,000), and the Caribbean (167.3/1,000; adjusted relative risk, 1.41; 95% CI, 1.36-1.46). For maternal ICU, the adjusted relative risk was 1.79 (95% CI, 1.43-2.24) for women from Africa and 2.21 (95% CI, 1.78-2.75) for women from the Caribbean. Specifically, mothers from Ghana (adjusted relative risk, 2.71; 95% CI, 1.75-4.21) and Jamaica (adjusted relative risk, 2.74; 95% CI, 2.12-3.53) were at highest risk of maternal ICU admission. The risk of both mother and newborn concurrently admitted to ICU was even more pronounced for Ghana and Jamaica. CONCLUSIONS: Women from Africa and the Caribbean and, in particular, Ghana and Jamaica, are at higher risk of admission to ICU around the time of delivery, as are their newborns.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".