[Contribution of fluorescence immunocytochemistry (uCyt+TM) in the postoperative surveillance of bladder cancer].
Bibliographic record
Abstract
OBJECTIVE: To assess 1) the value of fluorescence immunocytochemistry (uCyt+ test, DiagnoCure Inc., Quebec) in the detection of recurrent bladder tumour after transurethral resection (TUR) and 2) the predictive value of a positive uCyt+ test in patients with negative cystoscopy. MATERIAL AND METHODS: This study was based on 132 patients with a mean follow-up of 21.9 weeks after TUR. The initial tumours were pTa G1-2 in 66.7% of cases, and G3 in 28.8% of cases. Cystoscopy, urine cytology (UC) and uCyt+ test data were collected on the day of the first control visit (D0), and the patients were then reviewed at 6 and 12 months. All lesions detected on cystoscopy were biopsed. RESULTS: The mean sensitivity of UC was 47.4% and the mean sensitivity of uCyt+ was 73.7% (84.2% in combination). In patients with negative cystoscopy on D0, a positive uCyt+ test has no predictive value at 6 months. At 12 months, 20.0% of patients with positive UC had relapsed, versus 16.7% of patients with negative UC (p = ns). On the other hand, at 12 months, 50.0% of patients with negative cystoscopy but positive uCyt+ test had relapsed, versus 16.4% of patients with a negative uCyt+ test (p < 0.01). CONCLUSIONS: The uCyt+ test allows assessment of the risk of recurrence at I year, while UC alone only has a diagnostic value. These results raise the possibility of combining the tests in order to decrease the frequency of follow-up cystoscopy.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".