Risk stratification framework to improve the utility of renal ultrasound in acute kidney injury
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) is common among hospitalised patients and can lead to significant morbidity or mortality if not properly managed. Renal ultrasound (RUS) is often requested in the initial workup of AKI to rule out obstructive uropathy despite pre-renal aetiologies being implicated in most cases, especially in patients without risk factors for obstruction. Objectives: Determine the utility of RUS in detecting bilateral hydronephrosis in the context of AKI, and identify risk factors that can be used to stratify patients to better guide patient management. Method: Adults who underwent RUS for AKI between January 2019 and December 2021 were reviewed. Renal ultrasound studies that identified bilateral hydronephrosis and the patient characteristics associated with these studies were recorded. Results: Seven hundred and fifty-eight RUS reports were included. Bilateral hydronephrosis was diagnosed in 43 patients (5.7%). Of these 43 patients, 39 (90.7%) had at least one risk factor for urinary tract obstruction. Bilateral hydronephrosis was only diagnosed in 4 (9.3%) patients without any risk factor for obstruction. The risk factors with the highest odds for being diagnosed with bilateral hydronephrosis included a history of previous ureteric stenting or nephrostomy tube insertion (OR 10.37), previous bilateral hydronephrosis (OR 14.56), or multiple risk factors (OR 23.06). Conclusion: Renal ultrasound has limited utility in the evaluation of AKI in low-risk patients. Contribution: These risk factors can be used to assign patients to high- or low-risk categories to better guide management and reduce the number of unnecessary studies performed while still identifying clinically significant disease.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".