Bidirectional relationships between acute kidney injury and chronic kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Chronic kidney disease (CKD) remains one of the most potent predictors of acute kidney injury (AKI); however, recent epidemiologic studies have demonstrated a complex interplay between these two clinical entities. A growing body of evidence supports a bidirectional relationship: AKI leads to CKD, and the presence of CKD increases the risk of AKI. Additionally, several studies suggest that the presence of underlying CKD does modify the relation between AKI and adverse outcomes. In this article, we will review recent studies supporting the hypothesis that AKI leads to CKD and will explore the role of CKD as an effect modifier for AKI. RECENT FINDINGS: A recent meta-analysis confirms the association between AKI and the development of CKD and end-stage renal disease. Patient survival and renal outcomes after AKI are influenced by the presence of underlying CKD. AKI survivors with complete recovery of renal function remain at elevated risk of developing de-novo CKD, which may influence long-term survival; however, recovery of kidney function after AKI is associated with better long-term survival and renal function. SUMMARY: Recent findings support a strong association between AKI and CKD. There is uncertainty as to whether this relationship is causal. CKD is an effect modifier in AKI.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".