Low predictive value of positive transplant perfusion fluid cultures for diagnosing postoperative infections in kidney and kidney–pancreas transplantation
Bibliographic record
Abstract
AIMS: Infection following transplantation is a cause of morbidity and mortality. Perfusion fluid (PF) used to preserve organs between recovery and transplantation represents a medium suitable for the growth of microbes. We evaluated the relevance of positive growth from PF sampled before the implantation of kidney or kidney-pancreas (KP) allografts. METHODS: Between January 2007 and January 2011, 548 kidney/KP transplants were performed in our centre. A retrospective review of patient records with culture-positive PF was performed. RESULTS: PF was received from 483 (88%) patients, of which 35 (7%, 95% CI 5.3% to 9.9%) were positive for bacteria (31/483, 6.4%, 95% CI 4.6% to 9.8%) and fungi (4/483, 0.8%, 95% CI 0.3% to 2.1%). Thirty-two of the 35 culture-positive PF (91.4%, 95% CI 77.6% to 97%) were considered insignificant. The remaining three patients developed sepsis postoperatively, which was considered to be possibly related to growth in PF; Escherichia coli in one and Klebsiella pneumoniae in two. Of the non-skin flora bacteria cultured from PF, six were resistant to the prophylactic antibiotic given intraoperatively, but only one developed infection postoperatively (E coli, resistant to the co-amoxiclav). CONCLUSIONS: Significant attributable morbidity associated with PF-positive culture results was relatively rare. Culture of organisms other than Enterobacteriaceae or fungi are likely to represent contamination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".