MO061EARLY PERITONEAL DIALYSIS ATTRITION: PRELIMINARY RESULTS FROM THE PERITONEAL DIALYSIS OUTCOMES AND PRACTICE PATTERNS STUDY (PDOPPS)
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Early attrition from peritoneal dialysis (PD) may be an important source of modifiable PD treatment failure. Our primary objective was to gain understanding of early (< 120 days) discontinuation from PD by country, and to examine departure reasons. METHODS: The PDOPPS is a prospective cohort study of PD treatment and outcomes in Australia, Canada, Japan, New Zealand, Thailand, the United Kingdom (UK), and the United States (US). We analyzed 1251 patients enrolled to date within 120 days after first starting PD. Among patients discontinuing PD, the reasons were collected. The probability of PD discontinuation was estimated using Breslow estimator, accounting for left-truncation arising from some patients initiating dialysis prior to study enrollment. Patients were censored at transplant, transfer to another facility, recover renal function, loss to follow up, or reaching 120 days of PD vintage. MO061 Figure CONCLUSIONS: Across countries in PDOPPS, early attrition from PD varies by country and is largely driven by transfer to HD. Further incident patient recruitment and longer study follow-up will allow us greater understanding of occurrences and causes of PD discontinuation and its variation by country while identifying patient and facility predictors of early PD discontinuation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".