Prevalence of Urologic Disease Among Patients Investigated for Hematuria With CT Urography
Bibliographic record
Abstract
Purpose: The current study evaluated the prevalence of urologic disease among patients with hematuria referred for computerized tomography (CT) urography to determine which patients require investigation with CT urography. Methods: We retrospectively reviewed radiology reports of 1046 CT urograms performed for the indication of microscopic (43.7%) or gross hematuria (56.3%). Urological findings were categorized as negative, benign, or suspicious (pathologically confirmed) for malignancy. Results: Of 1046 CT urograms performed, 53.5% were negative, 36.4% were benign, and 10% were suspicious for malignancy. The most common benign finding was urolithiasis (22.3%). Overall, urinary tract malignancies were present in 3.6% of patients, and the rate was significantly higher ( P < .001) for gross (5.8%) than microscopic hematuria (0.9%). CT urography identified 0.6% patients with upper urinary tract malignancies; the malignancy rate was significantly higher ( P = .038) for gross (1%) than microscopic hematuria (0%), and no significant sex ( P = 1.00; male = 0.6%, female = 0.6%) or age ( P = .600; < 50 years = 0%, ≥ 50 years = 0.7%) differences were observed. Logistic regression revealed that being male was associated with gross hematuria (odds ratio [OR] = 2.92), and that both age and gross hematuria (ORs = 1.06 and 5.13, respectively) were associated with malignancy. Conclusions: CT urography found no upper urinary tract malignancies in 99.4% of patients presenting with hematuria, including all patients with microscopic hematuria and those with gross hematuria <50 years old. Investigating these subgroups with CT urography may be unnecessary and result in increased patient morbidity and health-care costs.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".