Exploring multidisciplinary practice patterns in the management of muscle invasive bladder cancer (MIBC) across the U.S. and Canada in 2015.
Bibliographic record
Abstract
368 Background: Effective treatment of MIBC requires a multidisciplinary (multiD) approach including surgical, radiation, and chemotherapeutic modalities, which are further supported by pathology, radiology, nursing, social work, nutrition, pharmacy, and psychologic expertise. As surgical and bladder preservation approaches have not been directly compared, practice patterns are heterogeneous. Our primary objective was to catalogue the different styles of multiD approaches or lack thereof in the management of MIBC. Secondary objectives were to assess physician interest, capture examples of successful approaches, and describe barriers to implementation. Methods: We conducted an international online survey regarding the type of multiD approach and available resources used by clinicians managing MIBC. We collaborated with the Bladder Cancer Advocacy Network and the Genitourinary Medical Oncologists of Canada to reach a wide variety of academic and community practices. Results: Of the 101 clinicians surveyed, most practiced at NCI designated comprehensive cancer centers (46%) or Canadian academic institutions (29%). The median number of cases per month was 5 (0-40).Of the different multiD styles, sequential (separate) visits on different days was the most common (60%) followed by sequential same day (41%), concurrent (one visit with all providers, 23%), and none (5%). However, most preferred a sequential same day (44%) or concurrent (28%) approach. Although most academic practices had some form of multiD approach, reported barriers for implementing the preferred strategy were lack of clinic space (63%), funding (46%), staff (46%), and time (34%). The majority felt a multiD approach enriched their practice. Conclusions: Most surveyed practitioners at academic centers integrate a multiD strategy in the management of MIBC. The major barriers are not attitudinal but rather insufficient resources and time. Thus, most physicians employ a sequential rather than a concurrent approach. Future goals include developing strategies to overcome these obstacles and integrating these results with patient preferences to optimize the management of MIBC patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".