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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".