Impact of timing of computed tomography staging and patient factors on the detection of ‘true’ <scp>cN</scp>+ bladder cancer
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether computed tomography (CT) scans should be performed before or after transurethral resection of bladder tumour (TURBT) for accurate lymph node staging in clinically lymph node-positive bladder cancer (BCa). Additionally, to identify patient factors that can aid in predicting lymph node metastasis. PATIENTS AND METHODS: In this retrospective, multicentre study, we analysed patients with cN+ M0 BCa staged by CT and treated with upfront radical cystectomy (RC) and pelvic lymph node dissection. We stratified patients by the interval between TURBT and CT into three groups: (1) before TURBT; (2) within 30 days after TURBT; and (3) more than 30 days post-TURBT. Staging accuracy, defined as concordance between clinical and pathological lymph node status, was evaluated. We utilised logistic regression analyses to identify patient factors, including the optimal timing of staging, in predicting pathological lymph node status at RC. RESULTS: Among 183 patients with cN+ disease, 90 (49%) had pN0 disease at RC. Of these, 40, 36 and 14 were staged before TURBT, within 30 days after TURBT, and more than 30 days post-TURBT, respectively (P = 0.2). Pathological downstaging was most common in cN1 (22%) and cN2 (20%) disease. The overall concordance rate was 23%. The timing of staging did not correlate with pathological lymph node status on logistic regression (all P > 0.05). Lymphovascular invasion (LVI) at TURBT was associated with pN status (odds ratio 4.25, confidence interval 2.02-9.34; P < 0.001) at RC. CONCLUSION: Overall, we found no association between the timing of CT-based staging and pathological lymph node metastases in cN+ BCa. The data suggest that performing a TURBT prior to staging does not increase the finding of false-positive nodes on imaging. LVI was the only factor at the time of TURBT associated with pathological lymph node metastasis at RC. Limitations include the multicentre retrospective design and the inclusion of only patients with clinically node-positive disease.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".