The impact of patient, physician and centre factors on successful treatment with peritoneal dialysis in individuals with end-stage kidney disease in Ontario: A population-based retrospective cohort study
Bibliographic record
Abstract
The use of peritoneal dialysis has been steadily declining over the past decade, and high treatment failure rates have been implicated. Past studies have examined clinical risk factors for peritoneal dialysis failure, but few have examined non-clinical, potentially modifiable risk factors. We conducted a retrospective cohort study of Ontario health care databases from April 1, 1995 to March 31, 2005 to examine the effect of patient, physician, and dialysis-centre characteristics on peritoneal dialysis treatment failure. In 5,162 incident peritoneal dialysis patients, the 5-year unadjusted probability of treatment and patient survival was 58.2% and 46.9%, respectively. In patients failing peritoneal dialysis, 43.5% failed treatment in the first year. Significant determinants of treatment failure included increased patient age, diabetes mellitus, lower level of education, greater frequency of nephrologist visits in the predialysis period, hemodialysis prior to peritoneal dialysis, male sex of the treating physician, and lower annual physician volume of peritoneal dialysis. Future strategies to improve peritoneal dialysis failure rates should include increasing educational resources and counselling available for patients with lower levels of education, increasing physician awareness about the poor outcomes associated with late nephrologist referral, and improving physician peritoneal dialysis volume and experience with peritoneal dialysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".