Point-of-Care Ultrasonography for the Diagnosis of Hydronephrosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Hydronephrosis is a common sign of urinary tract obstruction detected on imaging. The diagnostic test accuracy of point-of-care ultrasound (POCUS) for hydronephrosis is uncertain. Methods: We searched MEDLINE, EMBASE, and CENTRAL for observational studies and randomized controlled trials without language restrictions from inception until April 2024. We included studies reporting the diagnostic accuracy of POCUS for hydronephrosis compared to formal radiographic imaging as the reference standard (i.e., ultrasound, computed tomography, and/or intravenous pyelogram performed by a radiology technician or radiologist). We pooled sensitivity and specificity using random-effects models and reported their corresponding 95% confidence intervals (CIs). Results: We included 22 observational studies (n=4893). The pooled population included patients presenting with flank pain (13 studies), acute kidney injury (AKI) (2 studies), either flank pain or AKI (2 studies), or were unspecified (5 studies). Twenty studies were conducted in the emergency department, 1 study was conducted in hospital wards, and the remaining study was conducted in both an intensive care unit and hospital wards. POCUS had a pooled sensitivity of 0.83 (95% CI 0.79-0.87) and specificity of 0.80 (95% CI 0.74-0.86) for the diagnosis of hydronephrosis when compared to formal imaging. In a subgroup analysis of 3 studies (n=1078), the diagnosis of any severity of hydronephrosis on formal imaging based on moderate to severe hydronephrosis on POCUS demonstrated a pooled sensitivity of 0.32 (95% CI 0.28-0.37) and specificity of 0.96 (95% CI 0.93-0.98). Conclusion: POCUS has moderate sensitivity and specificity for hydronephrosis and may be used to rule out urinary tract obstruction in patients with a low pre-test probability. Moderate to severe hydronephrosis on POCUS is highly specific for hydronephrosis detected on formal imaging.Sensitivity and specificity forest plot
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".