Small Acute Increases in Serum Creatinine Are Associated with Decreased Long-Term Survival in the Critically Ill
Bibliographic record
Abstract
RATIONALE: Long-term outcomes after acute kidney injury (AKI) are poorly described. OBJECTIVES: We hypothesized that one single episode of minimal (stage 1) AKI is associated with reduced long-term survival compared with no AKI after recovery from critical illness. METHODS: A prospective cohort of 2,010 intensive care unit (ICU) patients admitted to the ICU between years 2000 and 2009 at a provincial tertiary care hospital. Development of AKI was determined according to the KDIGO classification and mortality up to 10 years after ICU admission was recorded. MEASUREMENTS AND MAIN RESULTS: Of the 1,844 eligible patients, 18.4% had AKI stage 1, 12.1% had stage 2, 26.5% had stage 3, and 43.0% had no AKI. The 28-day, 1-year, 5-year, and 10-year survival rates were 67.1%, 51.8%, 44.1%, and 36.3% in patients with mild AKI, which was significantly worse compared with the critically ill patients with no AKI at any time (P < 0.01). The unadjusted 10-year mortality hazard ratio was 1.53 (95% confidence interval, 1.2-2.0) for 28-day survivors with stage 1 AKI compared with critically ill patients with no AKI. Adjusted 10-year mortality risk was 1.26 (1.0-1.6). After propensity matching stage 1 AKI with no AKI patients, mild AKI was still significantly associated with decreased 10-year survival (P = 0.036). CONCLUSIONS: Patients with one episode of mild AKI have significantly lower long-term survival rates than critically ill patients with no AKI. Close medical follow-up of these patients may be warranted and mechanistic research is required to understand how AKI influences long-term events.
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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 | 0.004 |
| 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 | 0.001 |
| 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".