Birth defects among immigrants: Impact of exposure to a new environment, a 20 year population-based study
Bibliographic record
Abstract
Birth defects remain a global health issue. With increasing rates of migration, it is important to explore the role of immigration and the impact of new environments, especially food fortification, on birth defect prevalence. The prevalence of birth defects in the first year of life was compared between children born to immigrant and non-immigrant mothers in Ontario, Canada. Data on country of origin and arrival date were identified for immigrant mothers. The rate of birth defects among mothers coming from some countries was higher than Canadian mothers including 12 to 27% higher rates among mothers arriving from Sudan, Jamaica, Bangladesh and Afghanistan. The rate of birth defects among immigrant mothers who arrived before food fortification in 2000 was higher than the rate in non-immigrants, but after 2000 it was lower among immigrant mothers. Comparing the birth defect rate among two cohorts at one point in time could be misleading. Higher rates of birth anomalies among immigrant mothers who arrived before food fortification could be due to lack of access to folic acid in their country of origin. After food fortification, immigrant mothers likely had similar exposure to folic acid as non-immigrant mothers and their rate of was the same or lower. Key words: Birth defects, immigration, food fortification.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".