The long and the short of it – the impact of acute kidney injury in critically ill children
Bibliographic record
Abstract
Over the last 15 years, knowledge on acute kidney injury (AKI) epidemiology, risk factors, and outcomes has increased dramatically.In large part, this explosion in AKI research was made feasible by the development of standardized AKI definitions, including the Risk, Injury, Failure, End Stage Kidney Disease (RIFLE), the Acute Kidney Injury Network (AKIN), and most recently, the Kidney Disease: Improving Global Outcomes (KDIGO) definition. 1 In adults, within a few years of developing the first definition (RIFLE), strong evidence demonstrated that AKI, even mild AKI, was strongly associated with short-and long-term mortality.A few years later, many studies in adults showed the association of hospital-AKI with long-term cardiovascular events and incident/worsening chronic kidney disease (CKD), hypertension, end stage renal disease (ESRD), and other clinical outcomes.1 In adults, this research changed the landscape of AKI management.Indeed, the most recent KDIGO AKI guidelines (KDIGO is an international group which devel-
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.023 | 0.023 |
| 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".