Nephrology Follow-Up and Mortality of Critically Ill Patients with AKI
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) is common in the intensive care unit (ICU) and associated with adverse outcomes. We sought to estimate the association between nephrology follow-up within 3 months post-discharge and mortality in survivors of critical illness and moderate-to-severe AKI, compared to family physician-only follow-up. Methods: In this retrospective cohort study in Alberta, Canada, we identified adult patients admitted to ICU with KDIGO stage 2-3 AKI from 2005-2020 who survived to 3 months post-discharge without kidney replacement therapy or eGFR <15 mL/min/1.73 m2. Patients with nephrology follow-up were matched 1:1 on their propensity scores for nephrology vs. family physician-only follow-up within 3 months post-discharge. The primary outcome was death from 3 months post-discharge, reported as the cumulative incidence at 12, 24 and 50 months and hazard ratios (HR [95% confidence interval, CI]). Results: Of 8979 survivors of critical illness and stage 2-3 AKI at 3 months post-discharge, 500 (6%) received nephrology and 7455 (83%) received family physician-only follow-up. Outcome analysis included 437 patient pairs. Risks at 12, 24, and 50 months in patients with nephrology follow-up were 7%, 13%, and 26%, respectively, compared to 14%, 21%, and 36%, in patients with family physician-only follow-up (Figure 1). Nephrology follow-up was associated with lower mortality risk compared to family physician-only follow-up (HR 0.58 [95% CI: 0.44, 0.77] in the first 35 months and 1.09 [95% CI: 0.83, 1.42] after 35 months). Conclusion: In survivors of critical illness and stage 2-3 AKI, nephrology follow-up was associated with lower mortality, with potential benefits up to 3 years. Funding: Private Foundation SupportFigure 1. Cumulative incidence of death.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".