A Practical Guide for the Systemic Treatment of Biliary Tract Cancer in Canada
Bibliographic record
Abstract
Biliary tract cancers (BTC) are rare and aggressive tumors with poor prognosis. Radical surgery offers the best chance for cure; however, most patients present with unresectable disease, and among those receiving curative-intent surgery, recurrence rates remain high. While other locoregional therapies for unresectable disease may be considered, only select patients may be eligible. Consequently, systemic therapy plays a significant role in the treatment of BTC. In the adjuvant setting, capecitabine is recommended following curative-intent resection. In the neoadjuvant setting, systemic therapy has mostly been explored for downstaging in borderline resectable tumours, although evidence for its routine use is lacking. For advanced unresectable or metastatic disease, gemcitabine-cisplatin plus durvalumab has become the standard of care, while the addition of pembrolizumab to gemcitabine-cisplatin has also recently demonstrated improved survival compared to chemotherapy alone. Following progression on gemcitabine-cisplatin, several chemotherapy combinations and biomarker-driven targeted agents have been explored. However, the optimum regimen remains unclear, and access to targeted agents remains challenging in Canada. Overall, this article serves as a practical guide for the systemic treatment of BTC in Canada, providing valuable insights into the current and future treatment landscape for this challenging disease.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.101 | 0.041 |
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".