Bibliographic record
Abstract
PURPOSE OF REVIEW: Kidney transplant recipients are at high risk of contracting infections, some of which are considered vaccine-preventable, because of their highly immunosuppressed state. In this vulnerable group of patients, infection can lead to poor outcomes including graft failure and death, thus vaccination in the posttransplant population is an important strategy in order to mitigate this risk. The present review is aimed at providing an update on recent advances with respect to vaccination strategies in kidney transplant recipients. RECENT FINDINGS: General principles behind vaccination in kidney transplantation have remained consistent over many years. More recently, efforts have been focused on developing newer strategies for vaccination against influenza and herpes zoster in organ transplant recipients. Newer data on the immunogenicity of vaccines directed against pneumococcal disease, human papillomavirus, and hepatitis B virus in kidney transplant recipients have become available and will also be discussed in the present review. SUMMARY: Kidney transplant recipients are highly-vulnerable to contracting serious infections by way of their immunosuppressed state and their dampened ability to mount an immunogenic response to vaccines. Thus, ongoing advances in vaccination strategies in this group of patients should be an important area of focus of future research in order to help promote healthier living and greater survival postkidney transplant.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".