Intravesical bacillus Calmette-Guérin versus chemohyperthermia for high-risk non-muscle-invasive bladder cancer
Bibliographic record
Abstract
INTRODUCTION: Patients with high-risk non-muscle invasive bladder cancer (NMIBC) need adjuvant intravesical treatment after surgery. Although bacillus Calmette-Guérin (BCG) is highly effective, new adjuvant treatments to decrease recurrences and toxicity have been studies. We performed a retrospective propensity score-matched study to compare the efficacy of BCG and chemohyperthermia (C-HT). METHODS: We included 1937 patients diagnosed with bladder cancer between January 2004 and January 2014. The primary efficacy endpoint was recurrence-free interval. Patients treated with C-HT were matched with patients treated with BCG using propensity score-matched analysis. Cox-regression models were used to estimate the association between intravesical treatments and the presence of recurrence and progression. RESULTS: Of the 710 patients treated with intravesical treatments, 40 and 142 were eligible for inclusion in C-HT and BCG groups, respectively. Following case matching, there were no differences in patient or tumour characteristics between treatment groups. The 2-year recurrence-free interval in C-HT and BCG groups were 76.2% and 93.9%, respectively (p = 0.020). C-HT treatment (hazard ratio [HR] 5.42; 95% confidence interval [CI] 1.11-26.43; p = 0.036) and high-grade tumour (HR 4.60; 95% CI 1.01-20.88; p = 0.048) are associated with an elevated odds of tumour recurrence. In multivariate Cox-regression analysis, there was no significant difference between C-HT and BCG in the odds of recurrence (p = 0.054). There were no differences in progression between C-HT and BCG. CONCLUSION: C-HT is not as effective treatment as BCG in high-risk NMIBC patients who are BCG-naive. Although, there were no significant difference in the odds of recurrence, recurrence-free interval is significantly improved by the administration of BCG.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".