Systemic approaches in biliary tract cancers: a review in the era of multidirectional precision medicine
Bibliographic record
Abstract
INTRODUCTION: Despite a rising incidence, biliary tract cancers (BTCs) are still considered a rare tumor entity. The disease's subtle clinical presentation and lack of effective early detection strategies often lead to a diagnosis at an advanced or unresectable stage, where curative options are limited. AREAS COVERED: This review provides an overview of current systemic therapies and emerging novel approaches for BTC. For decades, the combination of gemcitabine with cisplatin (GemCis) has been the standard of care for palliative treatment. However, since 2020, the diagnostic and therapeutic landscape for BTC has evolved considerably, not only in the first-line setting but also beyond, driven by the development of clinical trials exploring immunotherapy and molecularly targeted agents. Due to the high frequency of targetable genetic alterations in BTC patients, there is a growing emphasis on obtaining tissue or liquid biopsy samples to identify markers like microsatellite instability and other actionable oncogenic driver genes. EXPERT OPINION: Early initiation of systemic therapies in combination with multimodal approaches is essential for maximizing survival outcomes in patients with BTC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".