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 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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".