Mortality Risk between For‐Profit and Not‐For‐Profit Dialysis Facilities
Bibliographic record
Abstract
Background: A recent meta‐analysis suggested that the relative risk of mortality is 8% higher in private for‐profit hemodialysis (HD) centers than private not‐for‐profit HD centers (Devereaux et al. JAMA 2002; 288: 2449). Objective: To assess the association between dialysis center profit status and mortality using USRDS data in the current era under DOQI guidelines. Methods: We studied incident HD patients from 1995 to 2000 who had Medicare as primary payer during the 4th through 6th month after ESRD initiation. Patients who died or underwent transplant during the 6 months after initiation were excluded. Primary dialysis provider was determined from the majority provider during the 6 months after initiation. Patient mortality for the following 12 months was evaluated via Cox regression, which adjusted for age, gender, primary cause of ESRD, race, cumulative hospital days, and comorbidity measures, which were assessed through both Medicare claims during the entry period and the Medicare Evidence form 2728. Patients were followed up until transplantation, death, or completion of the 12 months. Results: 189,932 patients receiving HD during 1995–2000 were included in our analysis. The adjusted mortality rate was 271.6 per 1000 person‐years at the private for‐profit centers and 272.2 at the private not‐for‐profit centers. The mortality risk relative to private for‐profit vs. not‐for‐profit centers was 1.009 (95% CI, 0.978–1.042; p = 0.558). Sub‐analyses that excluded hospital‐based facilities showed similar results. Although public not‐ for‐profit facilities were excluded from the analysis, a sub‐analysis performed with these also showed similar results. Conclusion: Our results suggest that dialysis center profit status was not associated with mortality. This lack of association was found in the combined 1995–2000 population and in separate models for each year. Freestanding vs. hospital‐based and public vs. non‐public facility had no impact on the results.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".