Measurement of birth outcomes in analyses of the impact of maternal influenza vaccination
Bibliographic record
Abstract
BACKGROUND: The estimated association of maternal influenza vaccination and birth outcomes may be sensitive to methods used to define preterm birth or small-for-gestational age (SGA). METHODS: In a cohort of pregnant women in Lao People's Democratic Republic, we estimated gestational age from: (a) date of last menstrual period (LMP), (b) any prenatal ultrasound, (c) first trimester ultrasound, (d) Ballard Score at delivery, and (e) an algorithm combining LMP and ultrasound. Infants were classified as SGA at birth using a Canadian, global, and equation-based growth reference. We estimated the association of maternal influenza vaccination and birth outcomes, by influenza activity, using multivariable log-binomial regression and Cox proportional hazards regression with vaccination as a time-varying exposure. RESULTS: The frequency of preterm birth in the cohort varied by method to estimate gestational age, from 5% using Ballard Score to 15% using any ultrasound. Using LMP, any ultrasound, or the algorithm, we found statistically significant reductions in preterm birth among vaccinated women during periods of high influenza activity and statistically significant increases in SGA, using a Canadian growth reference. We did not find statistically significant associations with SGA when using global or equation-based growth references. CONCLUSIONS: The association of maternal influenza vaccination and birth outcomes was most affected by the choice of a growth reference used to define SGA at birth. The association with pre-term birth was present and consistent across multiple statistical approaches. Future studies of birth outcomes, specifically SGA, should carefully consider the potential for bias introduced by measurement choice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".