Declining Mortality in Patients with Acute Renal Failure, 1988 to 2002
Bibliographic record
Abstract
Despite improvements in intensive care and dialysis, some experts have concluded that outcomes associated with acute renal failure (ARF) have not improved significantly over time. ARF was studied in hospitalized patients between 1988 and 2002 using the Nationwide Inpatient Sample, a nationally representative sample of discharges from acute-care, nonfederal hospitals. During a 15-yr period, 5,563,381 discharges with ARF and 598,768 with ARF that required dialysis (ARF-D) were identified. Between 1988 and 2002, the incidence of ARF rose from 61 to 288 per 100,000 population; the incidence of ARF-D increased from 4 to 27 per 100,000 population. Between 1988 and 2002, in-hospital mortality declined steadily in patients with ARF (40.4 to 20.3%; P < 0.001) and in those with ARF-D (41.3 to 28.1%; P < 0.001). Compared with 1988 to 1992, the multivariable-adjusted odds ratio (OR) of death was lower in 1993 to 1997 (ARF: OR 0.62, 95% confidence interval [CI] 0.61 to 0.64; ARF-D: OR 0.63, 95% CI 0.59 to 0.66) and 1998 to 2002 (ARF: OR 0.40, 95% CI 0.39 to 0.41; ARF-D: OR 0.47, 95% CI 0.45 to 0.50). The percentage of patients who had ARF with a Deyo-Charlson comorbidity index of 3 or more increased from 16.4% in 1988 to 26.6% in 2002 (P < 0.001). This study provides evidence from an administrative database that the incidence of ARF and ARF-D is rising. Despite an increase in the degree of comorbidity, in-hospital mortality has declined.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".