Quality, safety, and implementation science in acute kidney care
Bibliographic record
Abstract
PURPOSE OF REVIEW: Quality and safety are important themes in acute kidney care (AKC). There have been many recent initiatives highlighting these aspects. However, for these to become part of clinical practice, a rigorous implementation science methodology must be followed. This review will present these practices and will highlight recent initiatives in acute kidney injury (AKI), kidney replacement therapy (KRT) and recovery from AKI. RECENT FINDINGS: The 22nd Acute Disease Quality Initiative (ADQI) focused on achieving a framework for improving AKI care. This has led to various quality improvement (QI) initiatives that have been implemented following a robust implementation science methodology. In AKI, QI initiatives have been focused on implementing care bundles and early detection systems for patients at risk or with AKI. KRT initiatives have focused on measuring and reporting key performance indicators (KPIs), and providing targeted feedback and education to improve delivery of KRT. Finally, it has been recognized that post-AKI care is vitally important, and ongoing work has been focused on implementing pathways to ensure continuing kidney-focused care. SUMMARY: Quality and safety continue to be important focuses in AKC. Although recent work have focused on initiatives to improve these themes, additional work is necessary to further develop these items as we strive to improve the care to patients with 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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".