Early Peritonitis in A Large Peritoneal Dialysis Provider System in Colombia
Bibliographic record
Abstract
♦ BACKGROUND: Peritonitis is the most important complication of peritoneal dialysis (PD), and early peritonitis rate is predictive of the subsequent course on PD. Our aim was to calculate the early peritonitis rate and to identify characteristics and predisposing factors in a large nationwide PD provider network in Colombia. ♦ METHODS: This was a historical observational cohort study of all adult patients starting PD between January 1, 2012, and December 31, 2013, in 49 renal facilities in the Renal Therapy Services in Colombia. We studied the peritonitis rate in the first 90 days of treatment, its causative micro-organisms, its predictors and its variation with time on PD and between individual facilities. ♦ RESULTS: A total of 3,525 patients initiated PD, with 176 episodes of peritonitis during 752 patient-years of follow-up for a rate of 0.23 episodes per patient year equivalent to 1 every 52 months. In 41 of 49 units, the rate was better than 1 per 33 months, and in 45, it was better than 1 per 24 months. Peritonitis rates did not differ with age, ethnicity, socioeconomic status, or PD modality. We identified high incidence risk periods at 2 to 5 weeks after initiation of PD and again at 10 to 12 weeks. ♦ CONCLUSION: An excellent peritonitis rate was achieved across a large nationwide network. This occurred in the context of high nationwide PD utilization and despite high rates of socioeconomic deprivation. We propose that a key factor in achieving this was a standardized approach to management of patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".