Individualized acute kidney injury after care
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this study was to summarize the current evidence around the impact of individualizing patient care following an episode of acute kidney injury (AKI) in the ICU. RECENT FINDINGS: Over the last years, evidence has demonstrated that the follow-up care after episodes of AKI is lacking and standardization of this process is likely needed. Although this is informed largely by large retrospective cohort studies, a few prospective observational trials have been performed. Medication reconciliation and patient/caregiver education are important tenants of follow-up care, regardless of the severity of AKI. There is evidence the initiation and/or reinstitution of renin-angiotensin-aldosterone agents may improve patient's outcomes following AKI, although they may increase the risk for adverse events, especially when reinitiated early. In addition, 3 months after an episode of AKI, serum creatinine and proteinuria evaluation may help identify patients who are likely to develop progressive chronic kidney disease over the ensuing 5 years. Lastly, there are emerging differences between those who do and do not require renal replacement therapy (RRT) for their AKI, which may require more frequent and intense follow-up in those needing RRT. SUMMARY: Although large scale evidence-based guidelines are lacking, standardization of post-ICU-AKI is needed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".