Diagnostic Performance of Ultrasound for Macroscopic Hematuria in the Era of Multidetector Computed Tomography Urography
Bibliographic record
Abstract
PURPOSE: The objective of this study was to evaluate the diagnostic performance of ultrasound for detecting urinary tract neoplasm in the setting of macroscopic hematuria by using multidetector computed tomography urography (MDCTU) and cystoscopy as the reference standard. METHODS: This retrospective study was approved by our institutional review board. Patients with macroscopic hematuria who were investigated with an abdominal or renal ultrasound, an MDCTU, and a cystoscopy between January 2007 and December 2009, were eligible (95 patients). Exclusion criteria were time interval >12 months between index and reference tests or the absence of histopathologic proof of malignancy. Ultrasound results of the remaining 86 patients were collected and compared with the reference standard test, which was the combination of MDCTU for the assessment of upper urinary tract and cystoscopy for assessment of the lower urinary tract. The final diagnosis of neoplasm was based on pathologic findings. RESULTS: Urinary tract neoplasm was diagnosed in 20% of the patients (17/86). Sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios of ultrasound for detecting urinary tract neoplasms were 35.3% (6/17), 89.9% (62/69), 46.2% (6/13), 84.9% (62/73), 3.48 (95% confidence interval, 1.34-9.02), and 0.72 (95% confidence interval, 0.5-1.3), respectively. CONCLUSION: Sensitivity of ultrasound for the evaluation of macroscopic hematuria in the era of MDCTU is lower than expected. Results of our study suggest that patients with macroscopic hematuria should undergo MDCTU as first-line imaging modality, with little added benefit from ultrasound.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".