Regional Variations in the Prevalence of Major Congenital Malformations in Quebec: The Importance of Fetal Growth Environment.
Bibliographic record
Abstract
BACKGROUND: Congenital anomalies are the consequence of a complex interaction between genetic predisposition and fetal environment. Based on the Congenital Anomalies Surveillance in Canada Report, between 1998 and 2007 the rate of congenital heart defects in Quebec was significantly higher than the Canadian average; no data on the overall prevalence of congenital anomalies for Quebec or data on regional variations in any province are available. OBJECTIVES: To estimate the prevalence of major congenital malformations (MCMs) in all of the 17 administrative regions of Quebec. METHODS: Using data from the Quebec Pregnancy Cohort, we included infants if they were born between January 1, 1998 and December 31, 2008. MCMs were identified within the infant's first year of life using validated ICD-9 and ICD-10 codes. The rate of MCMs was calculated and stratified on Quebec's administrative regions. RESULTS: Among 152,353 eligible infants, the prevalence of MCMs was 36.6 (all rates were reported as per 1,000 live births). The regions with the highest rate of MCMs were Lanaudière (48.1), Laval (45.8), and Mauricie (45.1). Regions with the lowest rate were Outaouais (13.4), Côte-Nord (19.1), Abitibi-Témiscamingue (27.5), Gaspésie-îles-de-la-Madeleine (27.9), and Saguenay-Lac-Saint-Jean (28.9). Congenital heart defects (10.3) and musculoskeletal anomalies (12.6) were the most common. Laval had the highest rate of heart defects (16.1), and Lanaudière had the highest rate of musculoskeletal anomalies (22.0). CONCLUSIONS: The central regions of Quebec had high rate of MCMs, whereas the relatively genetically homogenous peripheral regions of Quebec had lower rate of MCM, suggesting the importance of fetal growth environment in the etiology of MCMs in Quebec.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".