Circulating Influenza Virus and Adverse Pregnancy Outcomes: A Time-Series Study
Bibliographic record
Abstract
Individual-level epidemiologic studies of pregnancy outcomes after maternal influenza are limited in number and quality and have produced inconsistent results. We used a time-series design to investigate whether fluctuation in influenza virus circulation was associated with short-term variation in population-level rates of preterm birth, stillbirth, and perinatal death in Ontario between 2003 and 2012. Using Poisson regression, we assessed the association between weekly levels of circulating influenza virus and counts of outcomes offset by the number of at-risk gestations during 3 gestational exposure windows. The rate of preterm birth was not associated with circulating influenza level in the week preceding birth (adjusted rate ratio = 1.01, 95% confidence interval: 1.00, 1.02) or in any other exposure window. These findings were robust to alternate specifications of the model and adjustment for potential confounding. Stillbirth and perinatal death rates were similarly not associated with gestational exposure to influenza circulation during late pregnancy. We could not assess mortality outcomes relative to early gestational exposure because of missing dates of conception for many stillbirths. In this time-series study, population-level influenza circulation was not associated with short-term variation in rates of preterm birth, stillbirth, or perinatal death.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".