Not Just an Oxymoron: The Utilitarian's Guide to Antimicrobial Stewardship in Transplant Infectious Diseases
Bibliographic record
Abstract
Solid organ transplant and hematopoietic cell transplant patients face an increased risk of infectious diseases, greater exposure to antibiotics, and heightened risk of multidrug-resistant organisms (MDROs) due to their immunosuppressed state. Antimicrobial stewardship programs (ASP) are essential in reducing the incidence of MDRO by conserving antimicrobial use, minimizing treatment durations, and improving the appropriate use of diagnostic testing. However, the role of ASP in transplant infectious diseases (TID) is still evolving, necessitating greater collaboration between ASP and transplant programs. This collaboration will mitigate infection risks, reduce infection-associated costs, and improve outcomes. This article reviews the key components for implementing ASP in TID, especially for those that are establishing or growing their ASP to include TID, including specific goals, structure and funding, ASP initiatives (including antibiotic allergy delabeling, diagnostic stewardship, and antiviral/antifungal stewardship), metrics, and educational opportunities.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".