Outcomes after acute kidney injury and critical illness
Bibliographic record
Abstract
PURPOSE OF REVIEW: Acute kidney injury (AKI) in critical illness is common, and survivors are faced with a host of adverse outcomes. In this article, we review the current landscape of outcomes and care in survivors of AKI and critical illness. RECENT FINDINGS: Follow-up care of survivors of AKI and critical illness is prudent to monitor for and mitigate the risk of adverse outcomes. Observational data have suggested improvement in outcomes with nephrology-based follow-up care, and recent interventional studies demonstrate similar findings. However, current post-AKI care is suboptimal with various challenges, such as breakdowns in the transition of care during hospital episodes and into the community, barriers for patients in follow-up, and lack of identification of high-risk patients for nephrology-based follow-up. Tools predictive of renal nonrecovery and long-term outcomes may help to identify high-risk patients who may benefit the most from nephrology-based care post-AKI. SUMMARY: Follow-up care of survivors of AKI and critical illness may improve outcomes and there is a need to prioritize transitions of care into the community. Further research is needed to elucidate the best ways to risk-stratify and manage post-AKI survivors to improve outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".