Prevention of Sepsis during the Transition to Dialysis May Improve the Survival of Transplant Failure Patients
Bibliographic record
Abstract
Dialysis patients are at risk for sepsis, and the risk may be even higher among transplant failure patients because of previous or ongoing immunosuppression. The incidence and the consequences of sepsis as defined by International Classification of Diseases, Ninth Revision, Clinical Modification hospital discharge diagnoses codes were determined among 5117 patients who initiated dialysis after transplant failure between 1995 and 2004 in the United States. The overall sepsis rate was 11.8 per 100 patient years (95% confidence interval [CI] 11.5 to 12.1). Sepsis was highest in the first 6 mo after transplant failure (35.6 per 100 patient years [95% CI 29.4 to 43.0] between 0 to 3 mo after transplant failure; 19.7 per 100 patient years [95% CI 17.2 to 22.5] between 3 to 6 mo after transplant failure). In comparison, the sepsis rate among incident dialysis patients between 3 and 6 mo after dialysis initiation was 7.8 per 100 patient years (95% CI 7.3 to 8.3), whereas the sepsis rate among transplant recipients between 3 and 6 mo after transplantation was 5.4 per 100 patient years (95% CI 4.9 to 5.9). Patients who were > or =60 yr, obese patients, patients with diabetes, and patients with a history or peripheral vascular disease or congestive heart failure were at risk for sepsis. Transplant nephrectomy was not associated with septicemia. The role of continued immunosuppression and vascular access creation was not assessed and should be addressed in future studies. In a multivariate analysis, patients who were hospitalized for sepsis had an increased risk for death (hazard ratio 2.93; 95% CI 2.64 to 3.24; P < 0.001). Strategies to prevent sepsis during the transition from transplantation to dialysis may improve the survival of patients with allograft failure.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".