Prospective evaluation of the prognostic relevance of molecular staging for urothelial carcinoma
Bibliographic record
Abstract
BACKGROUND: Nearly 50% of urothelial carcinoma patients with lymph node-negative invasive cancers recur after radical surgery. In many cases, occult local or lymph node disease may be present but undetectable by current approaches. Reverse-transcriptase polymerase chain reaction (RT-PCR)-detectable mRNA of Uroplakin II (UPII), a urothelial-specific gene mRNA, was evaluated in perivesical and lymph node samples removed at radical surgery as a predictor of clinical recurrence. METHODS: From November 1999 to August 2002, 46 patients with cTa-T4N0M0 urothelial bladder cancer enrolled in a prospective clinical trial and underwent radical cystectomy and pelvic lymphadenectomy. RT-PCR for UPII was performed on biopsies of the external surface of the bladder specimen and lymph nodes. Results were compared with conventional pathology. Patients were followed every 6 months for tumor recurrence. RESULTS: Pathologically node-negative patients had a UPII RT-PCR perivesical positivity of 27% and a lymph node positivity rate of 33%. All 22 UPII RT-PCR node-negative patients were pathologically node-negative and all 13 with pathologically positive nodes had positive UPII RT-PCR lymph node signals. In all, 46% of UPII RT-PCR lymph node-positive patients were pathologically node-negative and 5% of pathologically node-negative/UPII RT-PCR node-negative patients had disease recurrence, whereas 91% of pathologically node-negative/UPII RT-PCR node-positive patients (P < .001) recurred. UPII RT-PCR node positivity was a significant predictor of tumor recurrence in multivariate analysis CONCLUSIONS: Molecular determination of lymph node metastases by UPII RT-PCR node positivity apparently identifies patients with a poor prognosis and may be more predictive of disease recurrence than conventional pathologic analysis.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".