Found in Translation: Reasons for Optimism in the Pursuit to Prevent Chronic Kidney Disease After Acute Kidney Injury
Bibliographic record
Abstract
PURPOSE OF REVIEW: The current review will discuss on the progress of studying the transition phase between acute kidney injury (AKI) and chronic kidney disease (CKD) through improved animal models, common AKI and CKD pathways, and how human studies may inform different translational approaches. SOURCES OF INFORMATION: PubMed and Google Scholar. METHODS: A narrative review was performed using the main terms "acute kidney injury," "chronic kidney disease," "end-stage renal disease," "animal models," "review," "decision-making," and "translational research." KEY FINDINGS: The last decade has shown much progress in the study of AKI, including evidence of a pathophysiological link between AKI and CKD. We are now in a phase of redesigning animal models and discovering mechanisms that can replicate the pathological conditions of the AKI-to-CKD continuum. Translating these findings into the clinic is a barrier that must be overcome. To this end, current efforts include prediction of AKI onset and maladaptive repair, detecting patients susceptible to the progression of chronic maladaptive repair, and understanding shared signaling mechanisms between AKI and CKD. LIMITATIONS: This is a narrative review of the literature that is partially influenced by the knowledge, perspectives, and experiences of the authors and their research background. IMPLICATIONS: Overall, this new knowledge from the AKI-to-CKD continuum will help bridge the discontinuity that exists between animal models and patients, resulting in more effective translational biomarkers and therapeutics to test in known AKI pathologies thereby preventing the chronicity of kidney injury progression.
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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 | 0.045 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".