Cautery artifact understages urothelial cancer at initial transurethral resection of large bladder tumours
Bibliographic record
Abstract
INTRODUCTION: We sought to determine how frequently cautery (thermal) artifact precludes an accurate determination of stage at initial transurethral resection of bladder tumour (TURBT) of large bladder tumours. METHODS: We queried our institution's billing data to identify patients who underwent TURBT for large bladder tumours >5cm (CPT 52240) by two urologists at an academic centre from January 2009 through April 2013. Only patients who underwent initial-staging TURBT for urothelial cancer were included. Pathological reports were reviewed for stage, number of separate pathological specimens per TURBT, and presence of cautery artifact. Operative reports were reviewed for whether additional cold cup biopsies were taken of other suspicious areas of the bladder, resident involvement, and type of electrocautery. RESULTS: We identified 119 patients who underwent initial staging TURBT for large tumours. Cautery artifact interfered with accurate staging in 7/119 (6%) of cases. Of these, six patients underwent restaging TURBT, with 50% percent experiencing upstaging to T2 disease. Tumour size, tumour grade, whether additional cold cup biopsies were taken, number of separate pathological specimens sent, and resident involvement were not associated with cautery artifact (all p>0.05). Bipolar resection had a higher rate of cautery artifact 5/42 (12%), compared to monopolar resection 2/77 (2.6%) approaching significance (p=0.095). CONCLUSIONS: Cautery artifact may delay accurate staging at initial TURBT for large tumours by understaging up to 6% of patients.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".