Harmonization of epidemiology of acute kidney injury and acute kidney disease produces comparable findings across four geographic populations
Bibliographic record
Abstract
There is substantial variability in the reported incidence and outcomes of acute kidney injury (AKI). The extent to which this is attributable to differences in source populations versus methodological differences between studies is uncertain. We used 4 population-based datasets from Canada, Denmark, and the United Kingdom to measure the annual incidence and prognosis of AKI and acute kidney disease (AKD), using a homogenous analytical approach that incorporated KDIGO creatinine-based definitions and subsets of the AKI/AKD criteria. The cohorts included 7 million adults ≥18 years of age between 2011 and 2014; median age 59-68 years, 51.9-54.4% female sex. Age- and sex-standardised incidence rates for AKI or AKD were similar between regions and years; range 134.3-162.4 events/10,000 person years. Among patients who met either KDIGO 48-hour or 7-day AKI creatinine criteria, the standardised 1-year mortality was similar (30.4%-38.5%) across the cohorts, which was comparable to standardised 1-year mortality among patients who met AKI/AKD criteria using a baseline creatinine within 8-90 days prior (32.0%-37.4%). Standardised 1-year mortality was lower (21.0%-25.5% across cohorts) among patients with AKI/AKD ascertained using a baseline creatinine >90 days prior. These findings illustrate that the incidence and prognosis of AKI and AKD based on KDIGO criteria are consistent across 3 high-income countries when capture of laboratory tests is complete, creatinine-based definitions are implemented consistently within but not beyond a 90-day period, and adjustment is made for population age and sex. These approaches should be consistently applied to improve the generalizability and comparability of AKI research and clinical reporting.
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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 | 0.027 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".