Evaluation of autoimmune safety signal in observational vaccine safety studies
Bibliographic record
Abstract
Autoimmune safety evaluation is an important component of post-licensure vaccine safety evaluation. Recently, we published the findings from a large observational safety study of the quadrivalent human papillomavirus vaccine in females. From this study, based on two large managed care organizations, we have obtained some empirical data that may prove useful for the design of future vaccine safety studies within a managed care environment. For autoimmune conditions, a major challenge in vaccine safety study is to determine true incident cases in relation to the timing of vaccination. We found expert case review of medical records an indispensable component for autoimmune safety studies based on electronic health records. Case identification should also be expanded to include the use of laboratory test results or other relevant measures in addition to the disease specific ICD-9 diagnosis codes, when applicable. Furthermore, we recommend the parallel use of both safety signal evaluation that involves pattern evaluation for conditions that are more common, and statistical comparisons for conditions that are rather rare. Finally, we recommend an accompanying vaccine uptake study to understand the potential selection bias and confounding in a given study population that should be addressed with data collection and analytical techniques.
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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 | 0.540 | 0.783 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".