Comparison of Mortality Between Private For-Profit and Private Not-For-Profit Hemodialysis Centers
Bibliographic record
Abstract
CONTEXT: Private for-profit and private not-for-profit dialysis facilities provide the majority of hemodialysis care in the United States. There has been extensive debate about whether the profit status of these facilities influences patient mortality. OBJECTIVE: To determine whether a difference in adjusted mortality rates exists between hemodialysis patients receiving care in private for-profit vs private not-for-profit dialysis centers. DATA SOURCES: We searched 11 bibliographic databases, reviewed our own files, and contacted experts in June 2001-January 2002. In June 2002, we also searched PubMed using the "related articles" feature, SciSearch, and the reference lists of all studies that fulfilled our eligibility criteria. STUDY SELECTION: We included published and unpublished observational studies that directly compared the mortality rates of hemodialysis patients in private for-profit and private not-for-profit dialysis centers and provided adjusted mortality rates. We masked the study results prior to determining study eligibility, and teams of 2 reviewers independently evaluated the eligibility of all studies. Eight observational studies that included more than 500 000 patient-years of data fulfilled our eligibility criteria. DATA EXTRACTION: Teams of 2 reviewers independently abstracted data on study characteristics, sampling method, data sources, and factors controlled for in the analyses. Reviewers resolved disagreements by consensus. DATA SYNTHESIS: The studies reported data from January 1, 1973, through December 31, 1997, and included a median of 1342 facilities per study. Six of the 8 studies showed a statistically significant increase in adjusted mortality in for-profit facilities, 1 showed a nonsignificant trend toward increased mortality in for-profit facilities, and 1 showed a nonsignificant trend toward decreased mortality in for-profit facilities. The pooled estimate, using a random-effects model, demonstrated that private for-profit dialysis centers were associated with an increased risk of death (relative risk, 1.08; 95% confidence interval, 1.04-1.13; P<.001). This relative risk suggests that there are annually 2500 (with a plausible range of 1200-4000) excessive premature deaths in US for-profit dialysis centers. CONCLUSIONS: Hemodialysis care in private not-for-profit centers is associated with a lower risk of mortality compared with care in private for-profit centers.
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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 | 0.014 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".