Impact of Age on Peritonitis Risk in Peritoneal Dialysis Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite reductions in the frequency of peritoneal dialysis (PD)-related infectious complications over time, peritonitis and catheter infection remain important causes of morbidity and mortality. Given the increasing number of elderly patients reaching end-stage renal disease, making informed decisions about PD utilization is contingent on an understanding of the infectious complications of PD in this population. We therefore studied the impact of age on infection rates, organisms and outcomes. DESIGN, SETTING, PARTICIPANTS AND MEASUREMENTS: On the basis of data collected from 1996 to 2005 in the multicenter Baxter Peritonitis Organism Exit sites Tunnel infections database, the study population included 4247 incident Canadian PD patients: 1265 patients aged > or =70 yr and 2982 patients aged <70 yr. We defined two eras of PD initiation: 1996 to 2000 and 2001 to 2005. RESULTS: In a negative binomial model, older age was independently associated with a higher peritonitis rate (rate ratio [RR] 1.06 per decade increase; 95% CI 1.01 to 1.10; P = 0.008). However, this association was present only among those who initiated PD at an earlier time (RR 1.13 per decade increase; 95% CI 1.07 to 1.20; P < 0.001 in 1996 to 2000 versus 1.01 per decade increase; 95% CI 0.95 to 1.06; P = 0.81 in 2001 to 2005). Catheter-related infections were less frequent with increasing age regardless of era (RR 0.93 per decade increase; 95% CI 0.89 to 0.97). CONCLUSIONS: The higher peritonitis rate observed in elderly patients may represent an era effect, as age was not associated with peritonitis among patients initiating PD between 2001 and 2005. In addition, catheter infection was less frequent with increasing age.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".