The impact of histopathological evaluation at transurethral resection of bladder tumour on survival in radical cystectomy candidates
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of discordant histological diagnoses between transurethral resection of bladder tumour (TURBT) and radical cystectomy (RC) on cancer-specific mortality (CSM) in patients with bladder cancer (BCa). PATIENTS AND METHODS: We relied on a multi-institutional database collecting data of patients with BCa who underwent TURBT and subsequent RC from nine centres between 2000 and 2023. We tested concordance rates between TURBT and RC in detecting urothelial carcinoma of the urinary bladder (UCUB) as well as non-UCUB hystological subtypes, using RC as the reference standard. Concordance was defined as the agreement between a specific histological subtype identified both at TURBT and RC and evaluated according to Cohen's kappa coefficient. Subsequently, survival analyses consisted of Kaplan-Meier plots and multivariable Cox regression (MCR) models addressing CSM according to concordance between TURBT and RC (namely, concordant vs discordant). RESULTS: Overall, 3160 patients were identified. Of these, 2762 (87%) harboured UCUB and 398 (13%) non-UCUB at TURBT vs 2481 (79%) UCUB and 679 (21%) non-UCUB at RC. There were 683 (21.6%) patients with a discordant diagnosis between TURBT and RC. The overall concordance in detecting non-UCUB subtypes was defined as fair concordance (Cohen's kappa coefficient: 0.32). In MCR models, a discordant diagnosis exhibited higher CSM relative to those with a concordant diagnosis (hazard ratio [HR] 1.3, 95% confidence interval [CI] 1.1-1.6; P = 0.002). In a sensitivity analysis including patients with UCUB not exposed to neoadjuvant chemotherapy, this survival disadvantage was even higher (HR 1.5, 95% CI 1.1-1.7; P = 0.04). CONCLUSIONS: A discordant histopathological diagnosis between TURBT and RC is associated with higher CSM rates, particularly in cases initially misdiagnosed as UCUB. However, we also observed a moderate concordance between TURBT and RC in identifying non-UCUB subtypes.
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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 | 0.014 |
| 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 | 0.001 | 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".