Changes in the incidence and outcome for early acute kidney injury in a cohort of Australian intensive care units
Bibliographic record
Abstract
INTRODUCTION: There is limited information on whether the incidence of acute kidney injury (AKI) in critically ill patients has changed over time and there is controversy on whether its outcome has improved. METHODS: We interrogated the Australian New Zealand Intensive Care Society Adult Patient Database to obtain data on all adult admissions to 20 Australian intensive care units (ICUs) for >or= 24 hours from 1 January 1996 to 31 December 2005. Trends in incidence and mortality for ICU admissions associated with early AKI were assessed. RESULTS: There were 91,254 patient admissions to the 20 study ICUs, with 4,754 cases of AKI, for an estimated crude cumulative incidence of 5.2% (95% confidence interval, 5.1 to 5.4). The incidence of AKI increased during the study period, with an estimated annual increment of 2.8% (95% confidence interval, 1.0 to 5.6, P = 0.04). The crude hospital mortality was significantly higher for patients with AKI than those without (42.7% versus 13.4%; odds ratio, 4.8; 95% confidence interval, 4.5 to 5.1; P < 0.0001). There was also a decrease in AKI crude mortality (annual percentage change, -3.4%; 95% confidence interval, -4.7 to -2.12; P < 0.001), however, which was not seen in patients without AKI. After covariate adjustment, AKI remained associated with a higher mortality (odds ratio, 1.23; 95% confidence interval, 1.14 to 1.32; P < 0.001) and there was a declining trend in the odds ratio for hospital mortality. CONCLUSION: Over the past decade, in a large cohort of critically ill patients admitted to 20 Australian ICUs, there has been a significant rise in the incidence of early AKI while the mortality associated with AKI 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".