Test characteristics of point-of-care ultrasonography in patients with acute kidney injury
Bibliographic record
Abstract
Abstract Background Acute kidney injury is a common disorder that is associated with significant morbidity and mortality. Point-of-care ultrasonography (PoCUS) is an imaging modality performed at the bedside and is used to assess for obstructive causes of acute kidney injury. Little is known about the test characteristics of PoCUS in patients with acute kidney injury. Objective Our primary objective was to describe the test characteristics of PoCUS for the detection of hydronephrosis in patients presenting with acute kidney injury at our centre. Our secondary objective was to describe the current rate of use of PoCUS for this indication. Results In total, 7873 patients were identified between June 1, 2019 and April 30, 2021, with 4611 meeting inclusion criteria. Of these, 94 patients (2%) underwent PoCUS, and 65 patients underwent both PoCUS and reference standard, for a total of 124 kidneys included in our diagnostic accuracy analysis. The prevalence of hydronephrosis in our cohort was 33% (95% CI 25–41%). PoCUS had a sensitivity of 85% (95% CI 71–94%) and specificity of 78% (95% CI 68–87%) for the detection of hydronephrosis. Conclusion We describe the test characteristics of PoCUS for the detection of hydronephrosis in a cohort of patients with acute kidney injury. The low uptake of this test presents an opportunity for quality improvement work to increase its use for this indication.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".