Establishing a Core Outcome Measure for Peritoneal Dialysis-related Peritonitis: A Standardized Outcomes in Nephrology—Peritoneal Dialysis Consensus Workshop Report
Bibliographic record
Abstract
Introduction: Peritoneal dialysis (PD)-related peritonitis is one of the leading causes of discontinuation of PD and is considered a critically important outcome for patients on PD. However, there is no universally accepted method of measuring this outcome in clinical trials. Methods: We convened an online consensus workshop to establish a core outcome measure for PD-related peritonitis in clinical trials. Results: A total of 53 participants, including 18 patients and caregivers, from 12 countries engaged in breakout discussions in this workshop. Transcripts were analyzed thematically. We identified the following 3 themes: (i) feasibility and applicability across diverse settings, which reflected the difficulty with implementing laboratory-based measures in resource-limited environments; (ii) ensuring validity, which included minimizing false positives and considering the specificity of symptoms; and (iii) being meaningful and tangible to patients, which meant that the measure should be easy to interpret, reflect the impact that symptoms have on patients, and promote transparency by standardizing the reporting of peritonitis among dialysis units. Conclusion: A core outcome measure for PD-related peritonitis should include both symptom-based and laboratory-based criteria. Thus, the International Society for Peritoneal Dialysis (ISPD) definition of peritonitis is acceptable. However, there should be consideration of reporting suspected peritonitis in cases where laboratory confirmation is not possible. The measure should include all infections from the time of catheter insertion and capture both the rate of infection and the number of patients who remain peritonitis free. A core outcome measure with these features would increase the impact of clinical trials on the care and decision-making of patients receiving PD.
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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 | 0.309 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".