Adjudication of etiology of acute kidney injury: experience from the TRIBE-AKI multi-center study
Bibliographic record
Abstract
BACKGROUND: Adjudication of patient outcomes is a common practice in medical research and clinical trials. However minimal data exists on the adjudication process in the setting of Acute Kidney Injury (AKI) as well as the ability to judge different etiologies (e.g. Acute Tubular Necrosis (ATN), Pre-renal Azotemia (PRA)). METHODS: We enrolled 475 consecutive patients undergoing cardiac surgery at four sites of the Translational Research Investigating Biomarker Endpoints in AKI (TRIBE-AKI) study. Three expert nephrologists performed independent chart review, utilizing clinical variables and retrospective case report forms with pre intra and post-operative data, and then adjudicated all cases of AKI (n = 67). AKI was defined as a > 50% increase in serum creatinine for baseline (RIFLE Risk). We examined the patterns of AKI diagnoses made by the adjudication panel as well as association of these diagnoses with pre and postoperative kidney injury biomarkers. RESULTS: There was poor agreement across the panel of reviewers with their adjudicated diagnoses being independent of each other (Fleiss' Kappa = 0.046). Based on the agreement of the two out of three reviewers, ATN was the adjudicated diagnosis in 41 cases (61%) while PRA occurred in 13 (19%). Neither serum creatinine or any other biomarker of AKI (urine or serum), was associated with an adjudicated diagnosis of ATN within the first 24 post-operative hours. CONCLUSION: The etiology of AKI after cardiac surgery is probably multi-factorial and pure forms of AKI etiologies, such as ATN and PRA may not exist. Biomarkers did not appear to correlate with the adjudicated etiology of AKI; however the lack of agreement among the adjudicators impacted these results. TRIAL REGISTRATION: Clinicaltrials.gov: NCT00774137.
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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 | 0.137 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".