Ascertainment and Epidemiology of Acute Kidney Injury Varies with Definition Interpretation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Differences in defining acute kidney injury (AKI) may impact incidence ascertainment. We assessed the effects of different AKI definition interpretation methods on epidemiology ascertainment. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Two groups were studied at Texas Children's Hospital, Houston, Texas: 150 critically ill children (prospective) and 254 noncritically ill, hospitalized children receiving aminoglycosides (retrospective). SCr was collected for 14 d in the prospective study and 21 d in the retrospective study. Children with known baseline serum creatinine (bSCr) were classified by the pediatric Risk, Injury, Failure, Loss, End-Stage Kidney Disease (pRIFLE) AKI definition using SCr change (pRIFLE(DeltaSCr)), estimated creatinine clearance (eCCl) change (pRIFLE(DeltaCCl)), and the Acute Kidney Injury Network (AKIN) definition. In subjects without known bSCr, bSCR was estimated as eCCl = 100 (eCCl(100)) and 120 ml/min per 1.73 m(2) (eCCl(120)), admission SCr (AdmSCr) and lower/upper normative values (NormsMin, NormsMax). The differential impact of each AKI definition interpretation on incidence estimation and severity distribution was evaluated. RESULTS: pRIFLE(DeltaSCr) and AKIN led to identical AKI distributions. pRIFLE(DeltaCCl) resulted in 14.5% (critically ill) and 11% (noncritical) more patients diagnosed with AKI compared to other methods (P 0.05). Different bSCr estimates led to differences in AKI incidence, from 12% (AdmSCr) to 87.8% (NormsMin) (P 0.05) in the critically ill group and from 4.6% (eCCl(100)) to 43.1% (NormsMin) (P 0.05) in the noncritical group. CONCLUSIONS: AKI definition variation causes interstudy heterogeneity. AKI definition should be standardized so that results can be compared across studies.
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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 | 0.068 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".