Transient azotaemia is associated with a high risk of death in hospitalized patients
Bibliographic record
Abstract
BACKGROUND: There are no suitably powered epidemiological studies of 'transient azotaemia' (TA). The objective of this study was to describe the epidemiology of TA and its independent association with hospital mortality. We hypothesized that TA would be associated with an independent increase in the risk of death. METHODS: We retrospectively studied all patients admitted to a university-affiliated hospital in Australia between January 2000 and December 2002. Patients were excluded if they were <15 years old, were on chronic dialysis, had kidney transplant or if their length of hospital stay was <24 hours. We defined TA as rapidly recovering acute kidney injury (AKI) (return to no-AKI risk, injury, failure, loss, end stage (RIFLE) class within 72 hours of onset). We performed descriptive and comparative statistical analysis of data. The primary outcome of the study was the association between TA and hospital mortality in multivariate logistic regression analysis. RESULTS: Among 20 126 study patients, 3641 (18.1%) had AKI according to the RIFLE criteria and 1600 had AKI, which recovered during their hospital stay. Recovery of AKI occurred most commonly within 1 day after diagnosis (37.7%, n = 603). Furthermore, 1172 patients (73.3%) who recovered from AKI did so within 3 days (TA). After correcting for confounding factors, compared with patients without AKI, patients with TA had a significantly higher odds ratio for hospital mortality (2.26; 95% confidence interval: 1.85-2.76). CONCLUSIONS: Transient azotaemia is common in hospital patients, represents close to a third of all cases of AKI and is independently associated with a significantly higher risk of death.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".