Perioperative chemotherapy for upper tract urothelial carcinoma: A microsimulation Markov model.
Bibliographic record
Abstract
508 Background: Upper tract urothelial carcinoma (UTUC) accounts for less than 5% of all urothelial cancers. As a result, this disease is clinically understudied and there are no definitive recommendations regarding use and timing of peri-operative chemotherapy. The objective of this study was to create a decision model comparing three treatment pathways in UTUC: nephroureterectomy (NU) alone, neoadjuvant chemotherapy (NAC), and adjuvant chemotherapy (AC). Methods: A Markov microsimulation model was constructed using TreeAge Pro to compare treatment strategies for patients with newly diagnosed UTUC. Our primary outcome was quality adjusted life expectancy (QALE). Secondary outcomes included rates of adverse chemotherapy events, bladder cancer diagnoses, and crude survival. Markov cycle length was 3 months to mimic the follow up interval used in clinical practice for patients with UTUC. A systematic literature review was used to generate probabilities to populate the model. The base case was a 70-year-old patient with a radiographically localized upper tract tumor. Patients could have evidence of nodal disease, but no distant metastasis. Results: A total of 100,000 microsimulations were generated. NAC was preferred with an estimated QALE of 7.52 years versus 6.80 years with NU alone and 7.20 years with AC. Overall, 39.6% of patients in the AC group with invasive pathology received and were able to complete chemotherapy. A total of 37.5% of patients in the NAC group experienced an adverse chemotherapy event compared to 15.1% of patients in the AC group. Bladder cancer recurrence rates were 64.9%, 66.0%, and 67.1% over the patient’s lifetime in the NU, NAC, and AC groups, respectively. Conclusions: This study provides evidence to support the increased use of NAC in UTUC until robust randomized trials can be completed. While the use of NAC in this population appears favourable, the ultimate choice rests with the clinician and should be based on patient and tumor factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".