Evaluating the impact of the genitourinary multidisciplinary tumour board: Should every cancer patient be discussed as standard of care?
Bibliographic record
Abstract
INTRODUCTION: We sought to prospectively evaluate the effectiveness of the multidisciplinary tumour board (MTB) on altering treatment plans for genitourinary (GU) cancer patients. METHODS: All GU cancer patients seen at our tertiary care hospital are discussed at MTB. We prospectively collected data on adult patients discussed over a continuous, 20-month period. Physicians completed a survey prior to MTB to document their opinion on the likelihood of change in their patient's treatment plan. Logistic regression was used to asses for factors associated with a change by the MTB, including patient age or sex, malignancy type, the predicted treatment plan, and the provider's years of experience or fellowship training. RESULTS: A total of 321 cancer patients were included. Patients were primarily male (84.4%) with a median age of 67 (range 20-92) years old. Prostate (38.9%), bladder (31.8%), and kidney cancer (19.6%) were the most common malignancies discussed. A change in management plan following MTB was observed in 57 (17.8%) patients. The physician predicted a likely change in six (10.5%) of these patients. Multivariate logistic regression did not determine physician prediction to be associated with treatment plan change, and the only significant variable identified was a plan to discuss multiple treatment options with a patient (odds ratio 2.46; 95% confidence interval 1.09-9.54). CONCLUSIONS: Routine discussion of all urologic oncology cases at MTB led to a change in treatment plan in 17.8% of patients. Physicians cannot reliably predict which patients have their treatment plan altered. Selectively choosing patients to be presented likely undervalues the impact of a multidisciplinary approach to care.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".