Pregnancy outcome following rubella vaccination: A prospective controlled study
Bibliographic record
Abstract
The rubella virus is a potent human teratogen. Because the rubella vaccine is prepared with live virus, a high level of anxiety surrounds exposure in pregnancy. There is relatively scarce data on fetal risk following vaccination in pregnancy, and all of the available data were collected retrospectively. Our objective was to examine whether periconceptional exposure to rubella vaccine can cause the congenital rubella syndrome, and to compare the rate of major malformations and developmental milestones among offspring of women who received rubella vaccine 3 months pre- or post-conception to an unexposed comparison group. We collected prospectively and followed up 94 women who received rubella vaccination 3 months pre- or post-conception and a comparison group that consisted of 94 women who were counseled during pregnancy in a similar manner but were not exposed to known teratogens. The controls were matched for age, smoking, alcohol, and drug use. Not any of the women exposed to the vaccine gave birth to a child with congenital rubella syndrome. Rates of major malformations were similar in both groups as were birth weights and developmental milestones. In contrast, the rate of therapeutic abortions was higher in the exposed group (7.4% vs. 0%) (P < 0.05), due to fears of teratogenicity. We conclude that rubella vaccination in pregnancy does not appear to affect pregnancy outcome in general or cause congenital rubella syndrome in particular.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".