Peri-Operative Chemotherapy for Bladder Cancer: A Survey of Providers to Determine Barriers and Enablers
Bibliographic record
Abstract
Background: Utilization of chemotherapy for patients with muscle-invasive bladder cancer (MIBC) is low. In earlier qualitative work we used the Theoretical Domains Framework (TDF) to determine barriers and enablers of chemotherapy use. In this project we aimed to determine the prevalence of these barriers and enablers in Canadian physicians. Methods: Practicing Canadian urologists, medical oncologists (MOs) and radiation oncologists (ROs) participated in a specialty-specific web-based quantitative survey to assess potential barriers and enablers to chemotherapy use. Survey questions were developed that were thematically mapped to TDF domains. Logistic regression was used to identify TDF domains associated with high referral/use of chemotherapy. Results: 110 urologists, 47 MOs and 43 ROs completed the survey; response rates were 20%, 35% and 31% respectively. The mean reported survival gain associated with neoadjuvant chemotherapy (NACT) was 9%, 8%, and 7% for urologists, MOs, and ROs respectively. Among participating urologists, the TDF domains ‘ social and professional role’ (OR = 16.5, 95% CI 4.6–59.2), ‘ social influences’ (OR = 5.7, 95% CI 2.4–13.4) ‘ beliefs about consequences’ (OR = 4.9, 95% CI 1.8–13.3) and ‘ memory, attention and decision-making’ (OR = 0.50, 95% CI 0.27–0.91) were associated with MO referral rates. Among MOs, the TDF domains ‘ behavioural regulation’, ‘ social influences’, and ‘ social and professional role’ were associated with greater use of chemotherapy ( p < 0.05). No TDF domains were associated with RO referral to MO. Conclusions: We have identified several factors associated with referral/use of chemotherapy for MIBC. Optimization of multidisciplinary patient care needs to be considered when designing future interventions to close the gap between evidence and practice.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".