Vaccine package inserts and prescribing habits of obstetricians-gynecologists for maternal vaccination
Bibliographic record
Abstract
Despite ample evidence of the safety and efficacy of the influenza vaccine and the tetanus, diphtheria, and acellular pertussis (Tdap) vaccine during pregnancy, two-thirds of pregnant women do not receive these vaccines. Providers have a significant role in increasing prenatal vaccine uptake. It is important to understand how different sources of vaccine prescribing information, such as Food and Drug Administration package inserts, influence provider recommendations. We aimed to examine the role of vaccine package inserts in provider recommendations and perceptions of safety and effectiveness of vaccines during pregnancy. A cross-sectional survey was mailed to a random, weighted sample of American College of Obstetricians and Gynecologists Fellows living in the United States in March 2019. Providers were asked about their attitudes toward package inserts, and to evaluate sample package insert statements following two different labeling rules. Their evaluations of each rule were then compared. Of the 321 respondents, the majority (90%, 288/321) recommended and/or administered maternal vaccinations. Few respondents (7.8%, 25/321) read package inserts for information regarding vaccination. Respondents were less likely to recommend sample vaccines with Pregnancy and Lactation Labeling Rule-complying inserts (46.1%, 148/321) than vaccines with Pregnancy Category inserts (87.5%, 282/321). Although most providers did not actively utilize vaccine package inserts to inform recommendations, the previous Pregnancy Categories rule was preferred compared to the Pregnancy and Lactation Labeling Rule. Collaborative efforts to update inserts with current clinical practices for pregnancy would be valuable in reducing apprehensiveness around package inserts to generate safer and more cogent recommendations for pregnant women.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".