ISPD peritonitis guideline recommendations: 2022 update on prevention and treatment
Bibliographic record
Abstract
Peritoneal dialysis (PD)-associated peritonitis is a serious complication of PD and prevention and treatment of such is important in reducing patient morbidity and mortality. The ISPD 2022 updated recommendations have revised and clarified definitions for refractory peritonitis, relapsing peritonitis, peritonitis-associated catheter removal, PD-associated haemodialysis transfer, peritonitis-associated death and peritonitis-associated hospitalisation. New peritonitis categories and outcomes including pre-PD peritonitis, enteric peritonitis, catheter-related peritonitis and medical cure are defined. The new targets recommended for overall peritonitis rate should be no more than 0.40 episodes per year at risk and the percentage of patients free of peritonitis per unit time should be targeted at >80% per year. Revised recommendations regarding management of contamination of PD systems, antibiotic prophylaxis for invasive procedures and PD training and reassessment are included. New recommendations regarding management of modifiable peritonitis risk factors like domestic pets, hypokalaemia and histamine-2 receptor antagonists are highlighted. Updated recommendations regarding empirical antibiotic selection and dosage of antibiotics and also treatment of peritonitis due to specific microorganisms are made with new recommendation regarding adjunctive oral N-acetylcysteine therapy for mitigating aminoglycoside ototoxicity. Areas for future research in prevention and treatment of PD-related peritonitis are suggested.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".