Association between C‐reactive protein response to influenza vaccine during pregnancy and birth outcomes
Bibliographic record
Abstract
OBJECTIVE: A growing body of evidence suggests inflammatory markers can help predict poor outcomes in pregnancy. We evaluated C-reactive protein (CRP)-a key biomarker of inflammation-before and after a safe immune provocation (the seasonal influenza vaccine) during pregnancy. We evaluated predictors of the magnitude of response, as well as the association between CRP response and birth outcomes. METHODS: Nonrandomized prospective cohort trial measuring CRP before and 3 days after administering seasonal flu vaccine to low-risk obstetrical patients in Calgary, Alberta. RESULTS: We analyzed 27 prevaccination/postvaccination samples. Body mass index (BMI) was positively associated with CRP at Day 0, and women with higher prepregnancy BMI had a less robust response to vaccination than did leaner women. There was a strong positive association between CRP response and infant birth weight; women who had the greatest response to vaccination (by tertile) gave birth to babies that weighed, on average, 256.2 g more than babies born to women with the lowest response. CONCLUSIONS: Higher BMI in pregnant women was associated with higher baseline CRP and less pronounced CRP response to vaccination. Stronger CRP response was associated with higher birth weight. These findings underscore the potential value of a more dynamic approach to studying the regulation of inflammation during pregnancy and its implications for birth outcomes. This study was registered as a clinical trial in clinicaltrials.gov (ID: REB15/1418).
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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 | 0.004 |
| 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 | 0.001 |
| 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".