Kinetics of Decline of Maternal Measles Virus-Neutralizing Antibodies in Sera of Infants in France in 2006
Bibliographic record
Abstract
The optimal age for measles vaccination is an important health issue, since maternal antibodies may neutralize the vaccine antigen before a specific immune response develops, while delaying vaccination may increase the risk of complicated diseases in infants. However, measles vaccination impacts the duration of protection afforded by transplacental transfer of maternal antibodies: vaccination-induced maternal antibodies disappear faster than disease-induced antibodies. In order to maintain protection against measles in infants, it is important to monitor the dynamics of this phenomenon in vaccinated populations. To assess the current situation in France, a multicenter, prospective seroepidemiological study was conducted in seven French hospitals between October 2005 and January 2007. Maternal measles antibody concentrations from 348 infants 0 to 15 months old were measured using the plaque reduction neutralization assay. Geometric mean concentrations and the percentage of infants with maternal measles antibody concentrations above the protection threshold (>or=120 mIU/ml) were assessed according to age. Results show that after more than 20 years of routine measles vaccination in France, maternal measles-neutralizing antibodies decrease dramatically in French infants by 6 months of age, from 1,740 mIU/ml for infants 0 to 1 month old to 223 mIU/ml for infants 5 to 6 months old, and that 90% of infants are not protected against measles after 6 months of age. Infant protection against measles could be optimized both by increasing herd immunity through an increased vaccine coverage and by lowering the age of routine vaccination from 12 to 9 months.
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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 | 0.001 |
| 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 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".