The morbidity and cost implications of hemodialysis clinical performance measures
Bibliographic record
Abstract
Clinical performance measures, including dialysis dose, hemoglobin, albumin, and vascular access, are the focus of monitoring and quality improvement activities. However, little is known about the implications of clinical performance measures for hospital utilization and health care costs. We obtained clinical performance measures and hospitalization records for a national random sample of 10,650 hemodialysis patients and analyzed the relationship between changes in clinical performance measures and hospital utilization after adjustment for patient demographic and medical characteristics. Higher hemoglobin, higher albumin, and fistula or graft use were independently associated with fewer hospitalizations, fewer hospital days, and decreased Medicare inpatient reimbursement. For example, a 0.5 g/dL higher hemoglobin, a 0.25 g/dL higher albumin, fistula use, and graft use were associated with hospitalization rate ratios of 0.90 (95% confidence interval 0.85, 0.96), 0.64 (0.53, 0.77), 0.60 (0.52, 0.69), and 0.79 (0.71, 0.89), respectively. Moreover, there was a 2-3-fold variation in hospital utilization across end-stage renal disease networks that was still evident after adjustment for patient characteristics and clinical performance measures. Clinical performance measures, especially albumin and vascular access, are strongly associated with hospital utilization and health care costs. These results highlight the importance of targeting nutrition and vascular access in quality improvement efforts. The marked variation in hospital utilization across networks deserves further examination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".