Use of organs from donors with bloodstream infection, pneumonia, and influenza: Results of a survey of infectious diseases practitioners
Bibliographic record
Abstract
BACKGROUND: Potential organ donors may be admitted with an infection to an intensive care unit, or contract a nosocomial infection during their stay, increasing the risk of potential transmission to the recipient. Because of a lack of practice guidelines and large-scale data on this topic, we undertook a survey to assess the willingness of transplant infectious diseases (ID) physicians to accept such organs. METHODS: We performed a 10-question survey of ID providers from the American Society of Transplantation Infectious Disease Community of Practice to determine the scope of practice regarding acceptance of organs from donors with bloodstream infection, pneumonia, and influenza prior to organ procurement, as well as management of such infections following transplantation. RESULTS: Among 60 respondents to our survey, a majority indicated that organs would be accepted from donors bacteremic with streptococci (76%) or Enterobacteriaceae (73%) without evidence of drug resistance. Acceptance rates varied based on infecting organism, type of organ, and center size. Ten percent of respondents would accept an organ from a donor bacteremic with a carbapenem-resistant organism. Over 90% of respondents would accept an organ other than a lung from a donor with influenza on treatment, compared with 52% that would accept a lung in the same setting. CONCLUSIONS: This study is the first to our knowledge to survey transplant ID providers regarding acceptance of organs based on specific infections in the donor. These decisions are often based on limited published data and experience. Better characterization of the outcomes from donors with specific types of infection could lead to liberalization of organ acceptance practices across centers.
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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 | 0.007 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".