Both Patient and Facility Contribute to Achieving the Centers for Medicare and Medicaid Services' Pay-for-Performance Target for Dialysis Adequacy
Bibliographic record
Abstract
The Centers for Medicare and Medicaid Services (CMS) designated the achieved urea reduction ratio (URR) as a pay-for-performance measure, but to what extent this measure reflects patient characteristics and adherence instead of its intent to reflect facility performance is unknown. Here, we quantified the contributions of patient case-mix and adherence to the variability in achieving URR targets across dialysis facilities. We found that 92% of 10,069 hemodialysis patients treated at 173 facilities during the last quarter of 2004 achieved the target URR ≥65%. Mixed-effect models with random intercept for dialysis facility revealed a significant facility effect: 11.5% of the variation in achievement of target URR was attributable to the facility level. Adjusting for patient case-mix reduced the proportion of variation attributable to the facility level to 6.7%. Patient gender, body surface area, dialysis access, and adherence with treatment strongly associated with achievement of the URR target. We could not identify specific facility characteristics that explained the remaining variation between facilities. These data suggest that if adherence is not a modifiable patient characteristic, providers could be unfairly penalized for caring for these patients under current CMS policy. These penalties may have unintended consequences.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".