Underimmunization of the solid organ transplant population: An urgent problem with potential digital health solutions
Bibliographic record
Abstract
Solid organ transplant recipients are at risk for potentially life-threatening infections due to lifelong immunosuppression. Vaccine-preventable infections result in graft injury, morbidity, mortality, and significantly increased medical costs. Unfortunately, the majority of transplant recipients continue to be underimmunized at the time of transplant and thereafter. Given the rising rates of vaccine hesitancy and refusal in the general population, transplant recipients can no longer rely on herd immunity to protect them from vaccine-preventable infections. Novel tools are desperately needed to overcome transplant-specific immunization barriers to improve immunization rates in this high-risk population. Digital health technologies may offer a solution by addressing transplant-specific barriers: specifically, providing accurate information about vaccine safety, efficacy, and timing in the pre- and posttransplant periods; making a complete immunization record universally available and easily accessible; enabling communication between patients and multiple providers; and providing automated vaccine reminders to both patients and providers when vaccines are due using transplant-specific immunization guidelines. Digital health has transformed health care by empowering patients with their own health information and connecting patients, their providers, and public health officials. In doing so, it offers a potential platform to address and overcome the problem of underimmunization in the transplant population.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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; 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".