Stepped-Wedge Trial of Decision Support for Acute Kidney Injury on Surgical Units
Bibliographic record
Abstract
Introduction Acute kidney injury (AKI) is common in the perioperative setting and associated with poor outcomes. Whether clinical decision support improves early management and outcomes of AKI on surgical units is uncertain. Methods In this cluster-randomized, stepped-wedge trial, 8 surgical units in Alberta, Canada were randomized to various start dates to receive an education and clinical decision support intervention for recognition and early management of AKI. Eligible patients were aged ≥18 years, receiving care on a surgical unit, not already receiving dialysis, and with AKI. Results There were 2135 admissions of 2038 patients who met the inclusion criteria; mean (SD) age was 64.3 (16.2) years, and 885 (41.4%) were females. The proportion of patients who experienced the composite primary outcome of progression of AKI to a higher stage, receipt of dialysis, or death was 16.0% (178 events/1113 admissions) in the intervention group; and 17.5% (179 events/1022 admissions) in the control group (time-adjusted odds ratio, 0.76; 95% confidence interval [CI], 0.53–1.08; P = 0.12). There were no significant differences between groups in process of care outcomes within 48 hours of AKI onset, including administration of i.v. fluids, or withdrawal of medications affecting kidney function. Both groups experienced similar lengths of stay in hospital after AKI and change in estimated glomerular filtration rate (eGFR) at 3 months. Conclusion An education and clinical decision support intervention did not significantly improve processes of care or reduce progression of AKI, length of hospital stays, or recovery of kidney function in patients with AKI on surgical units.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".