Real-World Monitoring of COVID-19 Vaccines: An Industry Expert View on the Successes, Challenges, and Future Opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pharmacovigilance leaders from major vaccine developers describe the learnings from the coronavirus disease 2019 (COVID-19) pandemic in the area of pharmacovigilance and pharmacoepidemiology. The authors aim to raise awareness of the co-operation among vaccine developers, highlight common challenges, advocate for solutions, and propose recommendations for the future in the areas of real-world safety and effectiveness, safety reporting and evaluation, and regulatory submissions. To enable timely evaluation of real-world safety and effectiveness, multi-sponsor study platforms were implemented, resulting in quicker recruitment over wide geographical areas. Future gains could be derived by developing geographically flexible, common protocols and/or joint company-sponsored studies for multiple vaccines and a collective strategy to build low/middle-income country (LMIC) sentinel sites. Safety reporting, signal detection and evaluation was particularly challenging given the unprecedented number of adverse events reported. New methods were required to manage increased report volume while maintaining the ability to quickly identify and respond to new data that could impact the benefit-risk profile of each vaccine. Worldwide health authority submissions, requests for information and differing regulatory requirements imposed significant burden on regulators and industry. Industry consensus on the safety reporting requirements and joint meetings with regulatory authorities markedly reduced this burden for all stakeholders. The most impactful innovations should be undertaken rapidly and expanded to other vaccines and therapeutics, with a multi-stakeholder approach. The authors of this paper make future recommendations and have launched an initiative named BeCOME (Beyond COVID Monitoring Excellence) with a focus on actions in each of the highlighted areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it