Transplant center support for infectious diseases
Bibliographic record
Abstract
BACKGROUND: Transplant Infectious Diseases (TID) is a rapidly growing subspecialty, which has contributed significantly to improving patient outcomes after transplantation. Obtaining institutional support to implement programs that promote excellence in patient care remains a challenge for many non-surgical transplant-related specialties. METHOD: We surveyed the membership of the American Society of Transplantation Infectious Diseases Community of Practice to assess characteristics of individual transplant programs and delineate current patterns of institutional support of TID, with a goal of facilitating the exchange of innovative funding ideas between transplant programs. RESULTS: Of 53 questionnaires returned, 36 programs reported the existence of a dedicated TID service for adults. Of these, the ratio of dedicated TID providers to the number of solid organ transplant patients transplanted annually ranged from 15:1 to 259:1. A total of 21% of responding programs indicated that they received no support from their institution. Respondents from larger programs were more likely to receive some type of programmatic support. CONCLUSION: Given that the presence of expert TID input into patient care can improve outcomes through direct patient management and transplant team education, we suggest that continued support of the unbillable time contributed by TID practitioners is a critical part of ensuring excellent outcomes after transplantation.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.137 | 0.008 |
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".