Emergency department non-contrast computed tomography for suspicion of obstructive urolithiasis: Yield and consequences
Bibliographic record
Abstract
INTRODUCTION: We aimed to analyze patterns of referral, yield, and clinical implications of non-contrast computed tomography (NCCT) in the acute evaluation of flank pain suspected as obstructive urolithiasis (OU) in a high-volume emergency department (ED). METHODS: The study comprised 506 consecutive NCCTs performed in the ED over four months. Detection rates of OU, incidental, and alternative findings were calculated. Imaging signs suspicious for recent passage of stones were considered positive for OU, while renal stones without signs of obstruction were considered unrelated to the acute presentation. OU, other findings requiring hospitalization, and incidental findings warranting further workup were considered situations in which NCCTs were warranted. RESULTS: NCCTs confirmed an OU diagnosis in 162 (32%) patients and non-clinically significant nephrolithiasis in 125 (25%). They revealed other findings in 108 (21%) patients, including 42 (8%) with clinically significant incidental findings and 26 (5%) with alternative diagnoses requiring hospitalization. NCCTs were entirely negative in 111 (22%) patients. Corroboration of these outcomes, together with overlapping of OU, incidental, and alternative significant findings in some patients resulted in an overall justified NCCT request rate of 44%. CONCLUSIONS: The yield of NCCT performed in acute presentations of flank pain suspected as OU is relatively low, and over one-half of the scans are unwarranted. The pattern of requesting NCCT in the ED needs refinement to avoid abuse that may lead to radiation overexposure, psychological burden, physical harm, and financial overload.
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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 | 0.002 | 0.019 |
| 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 | 0.001 |
| 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".