Pathology review impacts clinical management of patients with T1‒T2 bladder cancer
Bibliographic record
Abstract
INTRODUCTION: We sought to evaluate the contemporary role of a pathology review on management implications of patients with bladder cancer. METHODS: A total of 98 consecutive specimens from transurethral resections in patients with suspected bladder tumours were reviewed at our institution by genitourinary pathologist. Patients were classified into risk groups according to pathology reports obtained before and after review. A management course was proposed according to local institutional practice patterns and main urological guidelines. RESULTS: Overall, 34.7% of pathological reviews had significant changes associated with management implications, the majority of which were due to changes in risk category (and/or stage). On review pathology, 12 patients were recommended radical cystectomy instead of conservative management and two patients avoided radical cystectomy. Six patients initially staged as T1 and whose staging did not change after review had a proposed change in management in the form of early cystectomy as a treatment option, as they were deemed very high-risk secondary to high-risk features (such as carcinoma in situ or lymphovascular invasion found on review). Ten patients initially staged as T2 demonstrated high-risk features on review. CONCLUSIONS: T2 bladder cancer patients. A complete initial pathological report has the potential to further decrease the discrepancy between initial and review reports.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".