Long-Term Decrease in Bladder Cancer Recurrence with Hexaminolevulinate Enabled Fluorescence Cystoscopy
Bibliographic record
Abstract
PURPOSE: We assessed the impact of hexaminolevulinate fluorescence cystoscopic detection of papillary, nonmuscle invasive bladder cancer on the long-term recurrence rate. MATERIALS AND METHODS: Long-term followup was assessed in 551 participants enrolled in a prospective, randomized study of fluorescence cystoscopy for Ta or T1 urothelial bladder cancer. In the original study 280 patients in the white light cystoscopy group and 271 in the fluorescence cystoscopy group were followed with cystoscopy for 3, 6 and 9 months after initial resection or until recurrence. A study extension protocol was done for long-term followup of these patients. RESULTS: Followup information was obtained for 261 of the 280 patients (93%) in the white light group and 255 of the 271 (94%) in the fluorescence group. Median followup in the white light and fluorescence groups was 53.0 and 55.1 months, and 83 (31.8%) and 97 patients (38%) remained tumor free, respectively. Median time to recurrence was 9.4 months in the white light group and 16.4 months in the fluorescence group (p = 0.04). The intravesical therapy rate was similar in the 2 groups (46% and 45%, respectively). Cystectomy was done in 22 of 280 cases (7.9%) in the white light group and in 13 of the 271 (4.8%) in the fluorescence group (p = 0.16). CONCLUSIONS: Hexaminolevulinate fluorescence cystoscopy significantly improves long-term bladder cancer time to recurrence with a trend toward improved bladder preservation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".