Underutilization of peritoneal dialysis: the role of the nephrologist's referral pattern
Bibliographic record
Abstract
BACKGROUND: The incidence of end-stage renal disease is increasing, placing a tremendous burden on health care resources. Peritoneal dialysis (PD) is cheaper than hemodialysis and has many potential advantages and few contraindications as an initial modality selection. This study examined differences in patient PD attempt rates between nephrologists using technique survival and mortality as outcomes. METHODS: We performed a retrospective review of the Manitoba Renal Program databases from January 2004 to January 2010. Analysis of 630 patients who commenced dialysis and had demographic data available was performed. A genetic matching algorithm was used to balance potential differences between patient characteristics. Each nephrologist was then compared against their peers to calculate a PD attempt rate. The highest attempt rate group was compared with the lowest. RESULTS: When comparing PD attempt rates between groups, all the results were significant. PD technique survival at >90 days showed no significant differences (P = 0.42). Patient mortality at >90 days was also not significant when comparing groups (P = 0.14). CONCLUSIONS: Our data suggest that when comparing the low- with high-attempt groups, the factors limiting PD utilization do not include on-site availability of PD, case mix, funding, patient location or reimbursement. Aggressive approaches of starting more patients on PD did not lead to lower technique survival or higher mortality rates. If the PD attempt rate was maximized, a significant amount of money and resources could be saved or directed toward helping a larger population without significant harm to patients.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".