Congenital microcephaly in Quebec: baseline prevalence, risk factors and outcomes in a large cohort of neonates
Bibliographic record
Abstract
OBJECTIVE: We assessed baseline prevalence, risk factors and outcomes of microcephaly in a large population of neonates. DESIGN: Retrospective cohort study. SETTING: All hospitals in the province of Quebec, Canada. PARTICIPANTS: 794 microcephalic and 1 944 010 non-microcephalic infants born between 1989 and 2012. MAIN OUTCOME MEASURES: Baseline prevalence of microcephaly and occurrence of other congenital anomalies. We estimated the association of (1) pregnancy risk factors including TORCH infections (toxoplasmosis, rubella, cytomegalovirus, herpes, other), exposure to teratogens, diabetes and maternal congenital anomalies with risk of microcephaly, and (2) microcephaly with risk of infant mortality and severe morbidity, adjusted for maternal characteristics. RESULTS: The overall prevalence of microcephaly was 4.1 per 10 000, ranging between 3.0 and 5.3 per 10 000 over time. Only 37% of microcephalic infants presented with other congenital anomalies. Maternal infection during pregnancy was the strongest risk factor, with 32 times the risk of microcephaly (prevalence ratio 32.38; 95% CI 22.42 to 46.75) compared with no infection. Exposure to teratogens was the next most important risk factor, with three times greater risk (prevalence ratio 3.10; 95% CI 2.37 to 4.07). Microcephaly was associated with 20 times the risk of infant mortality compared with no microcephaly (prevalence ratio 20.52; 95% CI 15.57 to 27.04) and significantly greater infant morbidity. CONCLUSIONS: In Canada, infectious exposure during pregnancy is a strong risk factor for microcephaly, and affected infants are at higher risk of poor birth outcomes. Better monitoring of microcephaly is needed in the event that Zika or other novel viruses affect future risk.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".