Limitations of ultrasound compared with computed tomography for kidney stone surveillance
Bibliographic record
Abstract
INTRODUCTION: Renal ultrasound (US) offers less radiation exposure than computed tomography (CT) for kidney stone surveillance but has lower sensitivity and specificity for nephrolithiasis diagnosis. Additionally, US may overestimate stone size, leading to unnecessary surgical interventions. Evidence on US performance for kidney stone surveillance is variable, making its clinical utility unclear. We aimed to assess US accuracy against CT and identify factors influencing US performance. METHODS: We performed a retrospective review of patients with known nephrolithiasis seen in urology clinic at Stanford who underwent both renal US and CT within 90 days for surveillance from January to December 2022. Patients with spontaneous stone passage or interventions were excluded. Stone characteristics were recorded, and statistical analysis compared the diagnostic accuracy of US and CT. RESULTS: A total of 107 patients and 128 stones were included, with a mean time difference of 25.7 days between US and CT. US sensitivity was 77%, with a positive predictive value (PPV) of 75% for stone detection. The PPV was only 59% for stones >4 mm by CT. Mean stone size was 8.7 mm on US vs. 5.5 mm on CT (p=0.02), with more pronounced overestimation in smaller stones and higher body mass index (BMI) (p<0.05). No significant differences in US performance were found by stone location, laterality, or time between scans. Differences in stone detection (p=0.01) and size (p=0.03) were associated with the individual performing the ultrasound. CONCLUSIONS: US performance is limited compared to CT and is influenced by stone size, BMI, and sonographer. Overestimation by US may lead to unnecessary interventions in up to 40% of patients with stones >4 mm.
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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 | 0.057 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".