White paper on antimicrobial stewardship in solid organ transplant recipients
Bibliographic record
Abstract
Antimicrobial stewardship programs (ASPs) have made immense strides in optimizing antibiotic, antifungal, and antiviral use in clinical settings. However, although ASPs are required institutionally by regulatory agencies in the United States and Canada, they are not mandated for transplant centers or programs specifically. Despite the fact that solid organ transplant recipients in particular are at increased risk of infections from multidrug-resistant organisms, due to host and donor factors and immunosuppressive therapy, there currently are little rigorous data regarding stewardship practices in solid organ transplant populations, and thus, no transplant-specific requirements currently exist. Further complicating matters, transplant patients have a wide range of variability regarding their susceptibility to infection, as factors such as surgery of transplant, intensity of immunosuppression, and presence of drains or catheters in situ may modify the risk of infection. As such, it is not feasible to have a "one-size-fits-all" style of stewardship for this patient population. The objective of this white paper is to identify opportunities, risk factors, and ASP strategies that should be assessed with solid organ transplant recipients to optimize antimicrobial use, while producing an overall improvement in patient outcomes. We hope it may serve as a springboard for development of future guidance and identification of research 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".