A Clinical Risk Prediction Tool for Peritonitis-Associated Treatment Failure in Peritoneal Dialysis Patients
Bibliographic record
Abstract
Abstract A tool to predict peritonitis-associated treatment failure among peritoneal dialysis (PD) patients has not yet been established. We conducted a multicentre, retrospective cohort study among 1,025 PD patients between 2006 and 2016 in Thailand to develop and internally validate such a tool. Treatment failure was defined as either a requirement for catheter removal, a switch to haemodialysis, or peritonitis-associated mortality. Prediction model performances were analysed using discrimination (C-statistics) and calibration (Hosmer-Lemeshow test) tests. Predictors were weighted to calculate a risk score. In total, 435 patients with 855 episodes of peritonitis were identified; 215 (25.2%) episodes resulted in treatment failure. A total risk score of 11.5 was developed including, diabetes, systolic blood pressure <90 mmHg, and dialysate leukocyte count >1,000/mm 3 and >100/mm 3 on days 3–4 and day 5, respectively. The discrimination (C-statistic = 0.92; 95%CI, 0.89–0.94) and calibration ( P > 0.05) indicated an excellent performance. No significant difference was observed in the internal validation cohort. The rate of treatment failure in the different groups was 3.0% (low-risk, <1.5 points), 54.4% (moderate-risk, 1.5–9 points), and 89.5% (high-risk, >9 points). A simplified risk-scoring scheme to predict treatment failure may be useful for clinical decision making regarding PD patients with peritonitis. External validation studies are needed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".