Management and prevention of varicella and measles infections in pediatric solid organ transplant candidates and recipients: An IPTA survey of current practice
Bibliographic record
Abstract
BACKGROUND: Varicella and measles infections can be life-threatening after solid organ transplantation (SOT) but may be preventable with live-attenuated vaccines (LAV). METHODS: This survey conducted in January 2019 among subscribers of the International Pediatric Transplantation Association listserv aimed to explore the current strategies to prevent and manage both infections in the pediatric SOT population, including recommending LAV after SOT. RESULTS: The answers given by 95 pediatric SOT healthcare workers show that these strategies are not yet optimal and call for further education. In particular, 59% of respondents are unnecessarily waiting for a SOT candidate to be >1 year of age to start administrating LAV before SOT. Interestingly, most respondents are willing to administer LAV after SOT (57%), and a fifth (21%) are already doing so, off-label. The survey queried the precautions taken to improve safety evaluations after LAV, and identified knowledge gaps and practitioners' concerns. CONCLUSION: The results of this survey could be used as a starting point for education and promotion of the safe administration of LAV in carefully selected SOT recipients; in turn, this would increase available data that would contribute to the development of evidence-based guidelines by the transplant societies and ultimately prevent these infections after SOT.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".