Population-based surveillance of severe microcephaly and congenital Zika syndrome in Canada
Bibliographic record
Abstract
PURPOSE: To estimate the minimum incidence of congenital Zika syndrome (CZS) and severe microcephaly in Canada and describe key clinical, epidemiological, aetiological and outcome features of these conditions. METHODS: Two separate national surveillance studies were conducted on CZS and severe microcephaly using the well-established Canadian Paediatric Surveillance Program from 2016 to 2019. Over 2700 paediatricians across Canada were surveyed monthly and asked to report demographic details, pregnancy and travel history, infant anthropometry, clinical features and laboratory findings of newly identified cases. Reports were reviewed to assign an underlying aetiology of severe microcephaly. Incidence rates were estimated using monthly live birth denominators. RESULTS: Thirty-four infants met the case definition for severe microcephaly and <5 met the case definition for CZS. The associated minimum incidence rates were 4.5 per 100 000 live births for severe microcephaly and 0.1-0.5 per 100 000 live births for CZS. Of severe microcephaly cases, 53% were attributed to genetic causes, 15% to infectious or ischaemic causes and 32% to unknown causes. The median head circumference-for-age Z-score at birth was -3.2 (IQR -3.8 to -2.6), and catch-up growth was often not achieved. Common clinical features included intracranial abnormalities (n=23), dysmorphology (n=19) and developmental delays (n=14). Mothers of infants with non-genetic aetiologies travelled during pregnancy more often (10/16) than mothers of infants with genetic aetiologies (<5/18; p<0.01). CONCLUSION: Severe microcephaly and CZS are both rare in Canada. Minimum incidence rates can be used as a baseline against which novel or re-emergent causes of severe microcephaly or CZS can be compared.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".