The Diagnostic Yield of CT Urography in the Workup of Hematuria With Negative Cystoscopy
Bibliographic record
Abstract
PURPOSE: To determine the diagnostic yield of computed tomography urography (CTU) in patients evaluated for hematuria with negative cystoscopy and to assess the added value of CTU when compared with ultrasound (US) in this patient population. METHODS: A retrospective study was conducted of patients who underwent CTU within 12 months of negative cystoscopy for workup of hematuria at our institution from January 2016 to December 2017. Computed tomography urography findings were recorded and compared to clinical diagnoses to determine diagnostic yield. Computed tomography urography and US findings were compared in patients who underwent both examinations. Patient characteristics (age, sex, smoking history, and hematuria subtype) were reported. RESULTS: A total of 657 patients met the inclusion criteria, including 108 patients aged 50 years and younger. No cause for hematuria was identified in 41% of patients overall and 58% of patients aged 50 years and younger. The most common diagnoses were benign prostatic hyperplasia and urolithiasis, accounting for 25% and 21% of patients, respectively; 0.6% of patients were diagnosed with an upper urinary tract malignancy, all older than 50 years. Although US was superior or equal to CTU for diagnosis in 83% of patients who underwent both examinations, US had a 0% sensitivity for detection of upper urinary tract malignancy. CONCLUSION: The low diagnostic yield of CTU and low prevalence of upper urinary tract malignancy in patients evaluated for hematuria with negative cystoscopy, particularly those aged 50 years and younger, call into question the appropriateness of multiphasic CTU as a first-line imaging modality in this population.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".