Evaluating the Accuracy of Computed Tomography of the Kidneys, Ureters, and Bladder Interpretation by Urology Trainees for Suspected Acute Nephrolithiasis
Bibliographic record
Abstract
Aim: This study aims to evaluate the interpretation accuracy of urology trainees in reporting computed tomography of the kidneys, ureters, and bladder (CT-KUB) compared with the formal radiology reports in patients with suspected acute nephrolithiasis. Methods: A sample of 12 consecutive CT-KUB scans for suspected acute nephrolithiasis was prospectively compiled and displayed using a software PACS viewer. 11 urology trainees, with an average of 24 months of urology specialist training, interpreted each scan. A total of 132 urology trainees’ reports were compared to the formal radiology reports for agreement in detecting key findings (presence or absence of stone disease), signs of urinary tract obstruction, clinically significant findings, and clinically non-significant findings. Results: There was a high level of agreement between urology trainees and radiologists for detecting key findings (98.4%) and clinically significant abnormalities (72.7%). There was less agreement in detecting all signs of urinary tract obstruction (56.2%) and non-clinically significant findings (36.8%). Conclusion: This study shows that urology trainees can accurately report CT KUB studies for key findings and clinically significant abnormalities. This may improve ongoing acute management and early patient discharge. However, their findings should be verified against formal radiological reports.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".