Strategies to Enhance Rehabilitation After Acute Kidney Injury in the Developing World
Bibliographic record
Abstract
Acute kidney injury (AKI) is independently associated with new-onset chronic kidney disease (CKD), end-stage kidney disease, cardiovascular disease, and all-cause mortality. However, only a minority of patients receive follow-up care after an episode of AKI in the developing world, and the optimal strategies to promote rehabilitation after AKI are ill-defined. On this background, a working group of the 18th Acute Dialysis Quality Initiative applied the consensus-building process informed by a PubMed review of English-language articles to address questions related to rehabilitation after AKI. The consensus statements propose that all patients should be offered follow-up within 3 months of an AKI episode, with more intense follow-up (e.g., <1 month) considered based on patient risk factors, characteristics of the AKI event, and the degree of kidney recovery. Patients should be monitored for renal and nonrenal events post-AKI, and we suggest that the minimum level of monitoring consist of an assessment of kidney function and proteinuria within 3 months of the AKI episode. Care should be individualized for higher risk patients, particularly patients who are still dialysis dependent, to promote renal recovery. Although evidence-based treatments for survivors of AKI are lacking and some outcomes may not be modifiable, we recommend simple interventions such as lifestyle changes, medication reconciliation, blood pressure control, and education, including the documentation of AKI in the patient's medical record. In conclusion, survivors of AKI represent a high-risk population, and these consensus statements should provide clinicians with guidance on the care of patients after an episode of AKI.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".