Diminished efficacy of bacille Calmette-Guérin among elderly patients with nonmuscle invasive bladder cancer
Bibliographic record
Abstract
Objective: Bacille Calmette-Guérin (BCG) is recommended as adjunctive therapy among patients with highrisk nonmuscle-invasive bladder cancer (BC).Given that immune response is attenuated with age, we set out to determine the impact of age on response to BCG. Materials and Methods: We searched our prospective bladder information system and limited our search to patients with incident BC completely resected at transurethral resection (TUR) who completed a full induction course of BCG.We then analyzed the impact of age on outcome.Age was analyzed both dichotomously (greater or less than 75 years) as well as by 10-year increments.The main outcomes were recurrence or progression-free survival.Log-rank and multivariable Cox proportional-hazard analyses, adjusting for clinical and pathologic features (age, multifocality, pathologic stage, grade and associated carcinoma in situ, maintenance, and restaging) were used.Results: This cohort included 238 patients.Baseline parameters were similar aside from tumor number.Progression-free survival differed between age groups when examined either dichotomously or via 10-year increments.The 2-year progression-free survival was 87% among patients < 75 years vs 65% in patients > 75 years (log rank P < 0.001).An age-dependent trend was noted when analyzed by 10-year increment (log-rank for trend P = 0.011).On multivariable analysis, age was an independent risk factor for progression (HR = 2.9, 95% CI 1.7-4.9).Recurrence-free survival was similar among age strata.Conclusion: We demonstrated that advanced age is associated with higher progression rates despite BCG.The care of BC in the elderly population is of increasing concern and should be addressed in a prospective clinical study.
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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) | 0.000 | 0.000 |
| 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".