Immunization practices among pediatric transplant hepatologists
Bibliographic record
Abstract
Vaccination of pediatric liver transplant candidates and recipients represents an opportunity to decrease infectious complications following transplant. Although vaccine recommendations exist, studies have shown that many transplant candidates and recipients are under-immunized. The goals of this study were to assess among pediatric transplant hepatologists: (i) current immunization practices before and after transplantation, (ii) involvement of an ID physician in the transplant evaluation, and (iii) perceptions about vaccine safety and barriers to immunization. An 80-item e-mail survey of pediatric transplant hepatologists at centers in the United States and Canada participating in the SPLIT consortium was conducted from December 2014 to March 2015. The overall response rate was 80% (73/91), representing 97% (32/33) of SPLIT centers. Fifty percent of programs routinely involved an ID physician in the transplant evaluation. Administration of palivizumab was routinely considered by 48% of hepatologists pre-transplant and by 41% post-transplant. Live vaccines were recommended by 26% of hepatologists after transplant. About 10% of hepatologists reported concern that live vaccines after transplant could induce rejection. There is wide variation in immunization practices among transplant hepatologists. Specific evidence-based protocols are needed to guide immunization practices in transplant candidates and recipients.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".