Considering Frailty in SARS-CoV-2 Vaccine Development: How Geriatricians Can Assist
Bibliographic record
Abstract
The COVID-19 pandemic has disproportionately impacted frail older adults, especially residents of long-term care (LTC) facilities. This has appropriately led to prioritization of frail older adults and LTC residents, and those who care for them, in the vaccination effort against COVID-19. Older adults have distinct immunological, clinical, and practical complexity, which can be understood through a lens of frailty. Even so, frailty has not been considered in studies of COVID-19 vaccines to date, leading to concerns that the vaccines have not been optimally tailored for and evaluated in this population even as vaccination programs are being implemented. This is an example of how vaccines are often not tested in Phase 1/2/3 clinical trials in the people most in need of protection. We argue that geriatricians, as frailty specialists, have much to contribute to the development, testing and implementation of COVID-19 vaccines in older adults. We discuss roles for geriatricians in ten stages of the vaccine development process, covering vaccine design, trial design, trial recruitment, establishment and interpretation of illness definitions, safety monitoring, consideration of relevant health measures such as frailty and function, analysis methods to account for frailty and differential vulnerability, contributions in regulatory and advisory roles, post-marketing surveillance, and program implementation and public health messaging. In presenting key recommendations pertinent to each stage, we hope to contribute to a dialogue on how to push the field of vaccinology to embrace the complexity of frailty. Making vaccines that can benefit frail older adults will benefit everyone in the fight against COVID-19.
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How this classification was reachedexpand
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".