Acute Kidney Injury: Preclinical Innovations, Challenges, and Opportunities for Translation
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is a clinically important condition that has attracted a great deal of interest from the biomedical research community. However, acute kidney injury AKI research findings have yet to be translated into significant changes in clinical practice. OBJECTIVE: This article reviews many of the preclinical innovations in acute kidney injury AKI treatment, and explores challenges and opportunities to translate these finding into clinical practice. SOURCES OF INFORMATION: MEDLINE, ISI Web of Science. FINDINGS: This paper details areas in biomedical research where translation of pre-clinical findings into clinical trials is ongoing, or nearing a point where trial design is warranted. Further, the paper examines ways that best practice in the management of AKI can reach a broader proportion of the patient population experiencing this condition. LIMITATIONS: This review highlights pertinent literature from the perspective of the research interests of the authors for new translational work in AKI. As such, it does not represent a systematic review of all of the AKI literature. IMPLICATIONS: Translation of findings from biomedical research into AKI therapy presents several challenges. These may be partly overcome by targeting populations for interventional trials where the likelihood of AKI is very high, and readily predictable. Further, specific clinics to follow-up with patients after AKI events hold promise to provide best practice in care, and to translate therapies into treatment for the broadest possible patient populations.
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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 | 0.037 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".