Association between AKI, Recovery of Renal Function, and Long-Term Outcomes after Hospital Discharge
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study aimed to determine if recovery of kidney function after AKI modifies the association between AKI during hospitalization and adverse outcomes after discharge. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The effect of renal recovery after AKI was evaluated in a population-based cohort study (n=190,714) with participants identified from a provincial claims registry in Alberta, Canada, between November 1, 2002 and December 31, 2007. AKI was identified by a two-fold increase between prehospital and peak in-hospital serum creatinine (SCr). Recovery was assessed using SCr drawn closest to 90 days after the AKI event. All-cause mortality and a combined renal outcome of sustained doubling of SCr or progression to kidney failure were evaluated. RESULTS: Overall, 3.7% of the participants (n=7014) had AKI, 62.7% of whom (n=4400) survived 90 days. In the 3231 patients in whom recovery could be assessed over a median follow-up of 34 months, 30.8% (n=1268) of AKI survivors died and 2.1% (n=85) progressed to kidney failure. Participants who did not recover kidney function had a higher risk for mortality and adverse renal outcomes when AKI participants who recovered to within 25% of baseline SCr were used as the reference group (adjusted mortality hazard ratio (HR), 1.26; 95% confidence interval, 1.10, 1.43) (adjusted renal outcomes HR, 4.13; 95% confidence interval, 3.38, 5.04). Mortality HR was notably higher when participants failed to recover within 55% of baseline. CONCLUSIONS: Renal recovery after AKI is associated with a lower risk of death or adverse renal outcomes after hospital discharge.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".