The initiation of a multidisciplinary bladder cancer clinic and the uptake of neoadjuvant chemotherapy: A time-series analysis
Bibliographic record
Abstract
INTRODUCTION: While level 1 evidence supports the use of neoadjuvant chemotherapy (NAC) for patients with muscle-invasive bladder cancer (MIBC), its uptake has been underwhelming, even in academic centres. Our aim was to determine if the initiation of a multidisciplinary bladder cancer clinic (MDBCC) in 2008 at our institution, where patients are assessed simultaneously by bladder cancer-focused urologists and radiation oncologists with easy access to a medical oncologist, was associated with an increased use of NAC. METHODS: Patients with MIBC initiating treatment between July 2000 and June 2013 were identified and classified by academic year (July 1 to June 30). Time-series analyses using interventional autoregressive integrated moving average (ARIMA) models with ramp intervention functions were then conducted. A sensitivity analysis was performed on clinical N0 patients. RESULTS: The cohort included 278 patients: 168 from 2000-2007 and 110 from 2008-2012 (academic years). Forty-two (15.1%) patients received NAC and 74 (26.6%) received adjuvant chemotherapy (AC). Overall the proportion of patients receiving NAC increased from 7.7% before the MDBCC to 47.6% in 2012 (Interventional ARIMA p=0.036). The results were similar when restricting to cN0 patients (p<0.001). NAC use gradually increased over time regardless of MDBCC attendance, although the proportion of patients receiving NAC appears to have risen more sharply among MDBCC attendees. CONCLUSIONS: At our institution, the initiation of the MDBCC was temporally associated with increased use of NAC. In addition to multidisciplinary collaboration, having a critical mass of NAC physician advocates and support from institutional leaders are essential to the uptake of NAC.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".