Deferring Risk: Limitations to the Evidence in Product Labels for Vaccine Use in Pregnancy
Bibliographic record
Abstract
Background: The gaps in clinical trial evidence about vaccination in pregnancy have serious implications for health care worker and public misunderstandings. Contradictions between National Immunization Technical Advisory Group (NITAG) recommendations and regulatory product labeling information contribute to misinformation about vaccine safety and effectiveness. Methods: A mixed methods approach that included a stakeholder consensus decision-making workshop and a national survey of Canadian health care providers (HCPs). Results: We identified knowledge gaps and serious limitations concerning the information in vaccine product labels. Stakeholders were troubled that some HCPs rely on regulatory product labels to inform their decisions without knowing their limitations in content. Our survey showed that HCPs were uncertain about the purpose of product labels and the evidence contained in them. Over a third of respondents incorrectly thought that product labels and NITAG recommendations are based on the same evidence and that the information they contain is regularly updated. Conclusions: Applying social risk theories, we show how such gaps in information defer responsibility for decisions about disease risk and vaccine safety from regulatory agencies and vaccine manufacturers onto HCPs and their clients. This may be especially relevant for COVID-19 and other emerging vaccines that are initially authorized for conditional or emergency use, and especially in understudied populations such as pregnant people. More frequent updating and alignment of robust, unbiased, and independently reviewed clinical trial and postmarket safety and effectiveness evidence with NITAG recommendations would allay HCP and public misunderstandings.
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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 | 0.705 | 0.846 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".