Four steps to standardize reporting of peritoneal dialysis technique failure: A proposed approach
Bibliographic record
Abstract
BACKGROUND: Technique failure is an important outcome measure in research and quality improvement in peritoneal dialysis (PD) programs, but there is a lack of consistency in how it is reported. METHODS: We used data collected about incident dialysis patients from 10 Canadian dialysis programs between 1 January 2004 and 31 December 2018. We identified four main steps that are required when calculating the risk of technique failure. We changed one variable at a time, and then all steps, simultaneously, to determine the impact on the observed risk of technique failure at 24 months. RESULTS: A total of 1448 patients received PD. Selecting different cohorts of PD patients changed the observed risk of technique failure at 24 months by 2%. More than one-third of patients who switched to hemodialysis returned to PD-90% returned within 180 days. The use of different time windows of observation for a return to PD resulted in risks of technique failure that differed by 16%. The way in which exit events were handled during the time window impacted the risk of technique failure by 4% and choice of statistical method changed results by 4%. Overall, the observed risk of technique failure at 24 months differed by 20%, simply by applying different approaches to the same data set. CONCLUSIONS: The approach to reporting technique failure has an important impact on the observed results. We present a robust and transparent methodology to track technique failure over time and to compare performance between programs.
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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 | 0.401 | 0.482 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".