Pregnancy Outcomes From the Pregnancy Registry of a Human Papillomavirus Type6/11/16/18 Vaccine
Bibliographic record
Abstract
OBJECTIVE: To better describe the safety profile of pregnancy exposures to the human papillomavirus (HPV) type 6/11/16/18 vaccine by acquiring and analyzing postmarketing data on pregnancy outcomes (ie, live births, abortions, fetal deaths, and congenital anomalies). METHODS: Enrollment criteria included an identifiable patient and health care provider from the United States, France, or Canada and exposure within 1 month before the date of onset of the last menstrual period or at any time during pregnancy. Outcomes of interest were pregnancy outcomes and birth defects. Prospectively reported cases (reported before the outcome of the pregnancy was known) were used for rate calculations. RESULTS: For the 517 prospective reports with known outcome, 451 (87.2%) were live births, including three sets of twins. Of 454 neonates, 439 (96.7%) were normal. The overall rate of spontaneous abortion was 6.9 per 100 outcomes (95% confidence interval [CI] 4.8-9.6). The prevalence of major birth defects was 2.2 per 100 liveborn neonates (95% CI 1.05-4.05). There were seven fetal deaths (1.5 per 100 outcomes, 95% CI 0.60-3.09). CONCLUSION: Rates of spontaneous abortions and major birth defects were not greater than the unexposed population rates. Although no adverse signals have been identified to date, the HPV6/11/16/18 vaccine is not recommended for use in pregnant women. LEVEL OF EVIDENCE: III.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".