From barriers to solutions: an expert-based algorithm for cholangiocarcinoma and other biliary tract cancers testing in the Era of precision oncology
Bibliographic record
Abstract
INTRODUCTION: Biliary tract cancer (BTC) comprises a group of aggressive malignancies with poor prognosis and limited therapeutic options. Next-generation sequencing (NGS) has advanced BTC management by identifying targetable genomic alterations. However, routine multigene NGS testing faces clinical, logistical, and economic barriers to widespread implementation. AREAS COVERED: A multidisciplinary panel of eight experts from Germany, France, the UK, Spain, and Italy convened to explore national challenges in NGS adoption and propose a structured molecular profiling approach. Discussions addressed pre-analytical tissue handling, sequencing strategies, and access limitations. EXPERT OPINION: Despite molecular advances, NGS access varies significantly across Europe. Barriers include suboptimal tissue sampling, restricted reimbursement, infrastructure gaps, and limited bioinformatics support. The panel recommends combined DNA and RNA sequencing as the ideal approach. In settings without NGS, referral to equipped centers is advised, with single-gene assays reserved for essential targets. This algorithm is a temporary yet practical guide to inform treatment decisions under current healthcare constraints, aiming to support equitable and informed care for BTC patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".