Suboptimal use of neoadjuvant chemotherapy in radical cystectomy patients: A population-based study
Bibliographic record
Abstract
INTRODUCTION: We aimed to assess contemporary rates of neoadjuvant chemotherapy (NC) use. METHODS: We relied on the Surveillance, Epidemiology and End Results (SEER)-Medicare database for non-metastatic, muscle-invasive (T2-T4a) urothelial carcinoma of the urinary bladder (UCUB) patients who underwent radical cystectomy (RC) between 1991 and 2009. Multivariable logistic regression analyses tested predictors of NC use, such as: T-stage, N-stage, year of diagnosis, age at diagnosis, gender, race, use of radiotherapy (RT), marital status, urban status, socioeconomic status, tumour grade, and Charlson comorbidity index (CCI). RESULTS: Overall, 5207 patients treated with RC were identified. Of those, 332 (6.4%) received NC. The rate of NC increased over time from 6.1% (1991) to 15.0% (2009) (p<0.001). In multivariable analyses, year of diagnosis (odds ratio [OR]: 4.7; p<0.001), lower T-stage (T3 vs. T2: OR: 0.7; p=0.003), married status (OR: 1.5; p=0.006), and younger age at diagnosis (≥80 vs. 66-69: OR: 0.6; p=0.006) were associated with a higher odds of NC; all represented independent predictors of NC use. Neither race nor CCI demonstrated statistical significance. CONCLUSIONS: We reported lower than anticipated overall (6.4%) use of NC. Nonetheless, the rate increased from 6.1% (1991) to 15.0% (2009). Older and unmarried individuals were less likely to receive NC. NC rates were higher in T2 UCUB patients. Some of the observed discrepancies, such as lower use in unmarried individuals, may require correction. Better adherence to guidelines should be encouraged and implemented, especially based on the confirmed benefits of NC according to randomized, controlled trials. The study is limited by a retrospective design and limited variables.
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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 | 0.003 |
| 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 | 0.000 | 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".