Virtual cystoscopy: the evaluation of bladder lesions with computed tomographic virtual cystoscopy
Bibliographic record
Abstract
PURPOSE: Our objective was to assess the accuracy of computed tomographic virtual cystoscopy (CTVC) in the detection of urinary bladder lesions. METHODS: Twenty-five patients were examined using CTVC. Bladder scanned using multislice CT at a slice thickness of 1 mm. The data were transferred to a workstation for interactive navigation using surface rendering. Findings obtained from CTVC were compared with results from conventional cystoscopy and with pathological findings. RESULTS: Thirty-eight lesions were identified. The smallest was 0.2 × 0.3 cm; the largest was 7 × 4.5 cm. Both CTVC and conventional cystoscopy were used. Conventional cystoscopy detected the same number of lesions that were detected by CTVC. On morphological examination, 26 of the lesions were polypoid, 7 were sessile and 5 were bladder wall-thickening. While one of the polypoid lesions was reported as an inverted papilloma, 2 of the 5 lesions that were identified as wall-thickening were malignant and 3 were benign. The sensitivity of using CTVC to identify neoplasias was 100%; the accuracy was 89%. CONCLUSION: Although the definitive diagnosis of some suspected urinary bladder tumours is only possible with conventional cystoscopy and biopsy, CTVC is a minimally invasive technique which provides beneficial information about urinary bladder lesions.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".