Pediatric AKI in the real world: changing outcomes through education and advocacy—a report from the 26th Acute Disease Quality Initiative (ADQI) consensus conference
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is independently associated with increased morbidity and mortality across the life course, yet care for AKI remains mostly supportive. Raising awareness of this life-threatening clinical syndrome through education and advocacy efforts is the key to improving patient outcomes. Here, we describe the unique roles education and advocacy play in the care of children with AKI, discuss the importance of customizing educational outreach efforts to individual groups and contexts, and highlight the opportunities created through innovations and partnerships to optimize lifelong health outcomes. METHODS: During the 26th Acute Disease Quality Initiative (ADQI) consensus conference, a multidisciplinary group of experts discussed the evidence and used a modified Delphi process to achieve consensus on recommendations on AKI research, education, practice, and advocacy in children. RESULTS: The consensus statements developed in response to three critical questions about the role of education and advocacy in pediatric AKI care are presented here along with a summary of available evidence and recommendations for both clinical care and research. CONCLUSIONS: These consensus statements emphasize that high-quality care for patients with AKI begins in the community with education and awareness campaigns to identify those at risk for AKI. Education is the key across all healthcare and non-healthcare settings to enhance early diagnosis and develop mitigation strategies, thereby improving outcomes for children with AKI. Strong advocacy efforts are essential for implementing these programs and building critical collaborations across all stakeholders and settings.
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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 | 0.279 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".