The Half-Life of Maternal Transplacental Antibodies in infants from mothers vaccinated with diphtheria, tetanus and pertussis: An individual participant data meta-analysis
Bibliographic record
Abstract
Aim: There are no reliable estimates of the half-lives of maternal antibodies to the antigens found in the primary series vaccines. We aimed to calculate the half-lives of passively acquired antibodies in infants born to mothers participating in studies of tetanus, diphtheria and acellular pertussis (Tdap) vaccination during pregnancy. We aimed to determine whether decay rates varied according to maternal age, birthweight, sex, socioeconomic status, country, or vaccine received. Methods: De-identified individual participant data from infants born to women taking part in 9 studies of maternal immunization, in 8 countries (UK, Belgium, Thailand, Vietnam, Canada, Pakistan, USA and the Netherlands) were combined. Blood samples were taken at two timepoints before any Tdap containing vaccines were received by the infant: at birth and at 2-months of age. Decay rates for each antigen were log2-transformed and meta-analysis performed. Half-lives were calculated by taking the reciprocal of the absolute value of the mean decay rates. Results: A total of 4,091 samples were included in the analysis and there was significant variation between studies. There was significant variation in the half-lives of the 6 antigens of interest (p<0.001), with estimates ranging from 28.1 days for diphtheria to 35.6 days for filamentous haemagglutinin. The decay of maternal antibodies did not significantly differ by country-level socioeconomic status, maternal age, sex, birthweight or maternal vaccination. Conclusion: Maternal antibodies decay at different rates for the different antigens, however the magnitude of the differences are small. Differences in laboratory techniques may account for some of the variability between studies.
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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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.055 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".