Mortality at For-Profit Versus Not-For-Profit Hemodialysis Centers: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
We conducted a systematic review and meta-analysis to assess differences in risk-adjusted mortality rates between for-profit (FP) and not-for-profit (NFP) hemodialysis facilities. We searched 10 databases for studies published between January 2001 to December 2019 that compared mortality at private hemodialysis facilities. We included observational studies directly comparing adjusted mortality rates between FP and NFP private hemodialysis providers in any language or country. We excluded evaluations of dialysis facilities that changed their profit status, studies with overlapping data, and studies that failed to adjust for patient age and some measure of clinical severity. Pairs of reviewers independently screened all titles and abstracts and the full text of potentially eligible studies, abstracted data, and assessed risk of bias, resolving disagreement by discussion. We included nine observational studies of hemodialysis facilities representing 1,163,144 patient-years. In pooled random-effects meta-analysis, the odds ratio of mortality in FP relative to NFP facilities was 1.07 (95% CI 1.04-1.11). Patients at FP hemodialysis facilities have 7 percent greater odds of death annually than patients with similar risk profiles at NFP facilities. Approximately 3,800 excess deaths might be averted annually if U.S. FP hemodialysis operators matched NFP mortality rates.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".