Incidence and outcomes of acute kidney injury in a referred chronic kidney disease cohort
Bibliographic record
Abstract
BACKGROUND: Whilst chronic kidney disease (CKD) has been identified as a risk factor for the development of acute kidney injury (AKI), little has been published about the incidence and outcomes of those acute injuries on chronic stable kidney disease and even less in a referred cohort of CKD patients followed up by nephrologists. METHODS: We followed up 6862 patients registered as CKD in British Columbia, Canada for a median time of 19.4 months after they achieved an estimated glomerular filtration rate (eGFR) value < or =30 mL/min/1.73 m(2). AKI was defined as a decrease in eGFR of > or =25% compared to a moving baseline eGFR within 25 days. RESULTS: Of the CKD patients, 44.9% had at least one AKI episode. Crude incidence rate for a first AKI event was 34.8 per 100 person-years. Older age [adjusted relative risks (RR) = 0.93 by 10 years, 95% confidence intervals (CI) = 0.90, 0.95] was associated with a lower risk of AKI. Of the patients, 15.3% died before dialysis and 18.1% initiated dialysis. AKI was associated with both a higher risk of death (adjusted RR = 2.32, 95% CI = 2.04, 2.64) and an increased risk of dialysis (adjusted RR = 2.33, 95% CI = 2.07, 2.61). CONCLUSIONS: In a referred CKD population, AKI was a frequent event and associated with higher risks of dialysis and mortality. The incidence of AKI appears to be less with older age in this population. Quantification of AKI incidence and its risk factors in different populations is important for clinicians and planners, so that appropriate identification, prevention and treatment strategies can be tested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".