Impact of a multidisciplinary tumor board in the treatment of genitourinary tumors: Real-world data from a referral center in Mexico.
Bibliographic record
Abstract
e19248 Background: International guidelines for genitourinary cancers recommend treatment decisions by a multidisciplinary tumor board (MTB). The benefits of a MTB include greater accuracy in staging and in the probability of receiving care in compliance with international clinical practice guidelines, greater access to clinical trials, better communication between treating physicians and cost-effective care with greater patient satisfaction, which could translate into better outcomes. Our objective was to assess the impact of a MTB in the management of patients with genitourinary tumors in a tertiary referral university center of México. Methods: We performed a retrospective analysis of all cases presented to the Genitourinary Tumor Committee of our hospital from March to August 2019. Results: A total of 84 patients were included in the analysis; of these 80% were men with a median age of 61 years. Of all the cases, 68% were first-time presentations with a median time from evaluation to presentation of 4 days. The most frequently discussed diagnoses were prostate, urothelial and renal cancer, each corresponding to about 28% of the sample. Forty-six percent of the cases presented were in metastatic disease. The median time for discussion of each case after its presentation was 10 minutes. Changes were made in the clinical stage and treatment plan proposed by the most responsible physician in 4% and 46% of the cases, respectively, achieving a unanimous consensus in 88%. After the MTB session, 29 patients were lost to medical follow-up and were not subsequently evaluated. Among the 55 patients who underwent reassessment, the recommendations of the MTB were applied in 92%. Conclusions: Discussion of urologic oncology cases at the MTB led to a change in the treatment plan in almost half of the patients. Although MTBs are an increasingly common practice in Mexico, this is the first study that describes the impact that these sessions have on the management of genitourinary tumors in our population. The high rate of loss to medical follow-up remains an important problem in developing countries, negatively affecting the prognosis of these 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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".