Prognosis for long-term survival and renal recovery in critically ill patients with severe acute renal failure: a population-based study
Bibliographic record
Abstract
INTRODUCTION: Severe acute renal failure (sARF) is associated with considerable morbidity, mortality and use of healthcare resources; however, its precise epidemiology and long-term outcomes have not been well described in a non-specified population. METHODS: Population-based surveillance was conducted among all adult residents of the Calgary Health Region (population 1 million) admitted to multidisciplinary and cardiovascular surgical intensive care units between May 1 1999 and April 30 2002. Clinical records were reviewed and outcome at 1 year was assessed. RESULTS: sARF occurred in 240 patients (11.0 per 100,000 population/year). Rates were highest in males and older patients (> or = 65 years of age). Risk factors for development of sARF included previous heart disease, stroke, pulmonary disease, diabetes mellitus, cancer, connective tissue disease, chronic renal dysfunction, and alcoholism. The annual mortality rate was 7.3 per 100,000 population with rates highest in males and those > or = 65 years. The 28-day, 90-day, and 1-year case-fatality rates were 51%, 60%, and 64%, respectively. Increased Charlson co-morbidity index, presence of liver disease, higher APACHE II score, septic shock, and need for continuous renal replacement therapy were independently associated with death at 1 year. Renal recovery occurred in 78% (68/87) of survivors at 1 year. CONCLUSION: sARF is common and males, older patients, and those with underlying medical conditions are at greatest risk. Although the majority of patients with sARF will die, most survivors will become independent from renal replacement therapy within a year.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".