Integrating Molecular Insights into Biliary Tract Cancer Management: A Review of Personalized Therapeutic Strategies
Bibliographic record
Abstract
Biliary tract cancers (BTCs) are rare and aggressive malignancies with an increasing incidence and poor prognosis. The standard systemic treatment for BTCs has evolved to include immune checkpoint inhibitors associated with gemcitabine–cisplatin as first-line therapies. However, survival rates remain low, highlighting the critical need for personalized treatment strategies based on molecular profiling. Currently, significant advancements have been made in the molecular characterization of BTCs, where genetic alterations, such as IDH1 mutations and FGFR2 fusions, provide targets for therapy. Molecular profiling is crucial early in the management process to identify potential candidates for clinical trials and guide treatment strategy. The integration of these molecular insights into clinical practice has allowed for the development of targeted therapies, although many of them are still in the phase 2 trial stage without definitive survival benefits demonstrated in phase 3 trials. This integration of comprehensive molecular profile insights with traditional treatment approaches offers a new horizon in the personalized medicine landscape for BTCs, with the aim of significantly improving patient outcomes through precision oncology.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".