Risk of adverse outcomes among infants of immigrant women according to birth-weight curves tailored to maternal world region of origin
Bibliographic record
Abstract
BACKGROUND: Infants of immigrant women in Western nations generally have lower birth weights than infants of native-born women. Whether this difference is physiologic or pathological is unclear. We determined whether the use of birth-weight curves tailored to maternal world region of origin would discriminate adverse neonatal and obstetric outcomes more accurately than a single birth-weight curve based on infants of Canadian-born women. METHODS: We performed a retrospective cohort study of in-hospital singleton live births (328,387 to immigrant women, 761,260 to nonimmigrant women) in Ontario between 2002 and 2012 using population health services data linked to the national immigration database. We classified infants as small for gestational age (<10th percentile) or large for gestational age (≥90th percentile) using both Canadian and world region-specific birth-weight curves and compared associations with adverse neonatal and obstetric outcomes. RESULTS: Compared with world region-specific birth-weight curves, the Canadian curve classified 20 431 (6.2%) additional newborns of immigrant women as small for gestational age, of whom 15,467 (75.7%) were of East or South Asian descent. The odds of neonatal death were lower among small-for-gestational-age infants of immigrant women than among those of nonimmigrant women based on the Canadian birth-weight curve (adjusted odds ratio [OR] 0.83, 95% confidence interval [CI] 0.72-0.95), but higher when small for gestational age was defined by the world region-specific curves (adjusted OR 1.24, 95% CI 1.08-1.42). Conversely, the odds of some adverse outcomes were lower among large-for-gestational-age infants of immigrant women than among those of nonimmigrant women based on world region-specific birth-weight curves, but were similar based on the Canadian curve. INTERPRETATION: World region-specific birth-weight curves seemed to be more appropriate than a single Canadian population-based curve for assessing the risk of adverse neonatal and obstetric outcomes among small- and large-for-gestational-age infants born to immigrant women, especially those from the East and South Asian regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".