Training in transplant infectious diseases: A survey of infectious diseases and transplant infectious diseases fellows in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: Infectious diseases (ID) specialists with experience in managing infections in transplant recipients and other immunocompromised hosts are increasingly needed as these fields expand. METHODS: To evaluate experiences and identify trainee-described needs in transplant infectious diseases (TID) training, the American Society of Transplantation, Infectious Diseases Community of Practice (AST IDCOP) surveyed ID fellows across the United States and TID fellows in the United States and Canada and received responses from 203 ID fellows and 13 TID fellows. RESULTS: Among ID fellows, the amount of TID training during ID fellowship was rated between less than ideal and adequate. Reasons cited included limited frequency of didactic activities and limited exposure to transplant patients during training. In particular, ID fellows at low-volume transplantation centers expressed interest in more TID training time, away training opportunities, and specific TID didactics. Educational resources of high interest among trainees were case-based interactive websites, mobile phone applications with TID guidelines, and a centralized collection of relevant articles. Pediatric ID fellows reported lower satisfaction scores with TID training, while TID fellows were overall satisfied or more than satisfied with their training experience. CONCLUSION: Findings from this survey will inform local and national TID educational initiatives.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".