Causes of Death after a Hospitalization with AKI
Bibliographic record
Abstract
Mortality after AKI is high, but the causes of death are not well described. To better understand causes of death in patients after a hospitalization with AKI and to determine patient and hospital factors associated with mortality, we conducted a population-based study of residents in Ontario, Canada, who survived a hospitalization with AKI from 2003 to 2013. Using linked administrative databases, we categorized cause of death in the year after hospital discharge as cardiovascular, cancer, infection-related, or other. We calculated standardized mortality ratios to compare the causes of death in survivors of AKI with those in the general adult population and used Cox proportional hazards modeling to estimate determinants of death. Of the 156,690 patients included, 43,422 (28%) died in the subsequent year. The most common causes of death were cardiovascular disease (28%) and cancer (28%), with respective standardized mortality ratios nearly six-fold (5.81; 95% confidence interval [95% CI], 5.70 to 5.92) and eight-fold (7.87; 95% CI, 7.72 to 8.02) higher than those in the general population. The highest standardized mortality ratios were for bladder cancer (18.24; 95% CI, 17.10 to 19.41), gynecologic cancer (16.83; 95% CI, 15.63 to 18.07), and leukemia (14.99; 95% CI, 14.16 to 15.85). Along with older age and nursing home residence, cancer and chemotherapy strongly associated with 1-year mortality. In conclusion, cancer-related death was as common as cardiovascular death in these patients; moreover, cancer-related deaths occurred at substantially higher rates than in the general population. Strategies are needed to care for and counsel patients with cancer who experience AKI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".