{"id":"W4411739445","doi":"10.1080/14737159.2025.2521538","title":"From barriers to solutions: an expert-based algorithm for cholangiocarcinoma and other biliary tract cancers testing in the Era of precision oncology","year":2025,"lang":"en","type":"review","venue":"Expert Review of Molecular Diagnostics","topic":"Cholangiocarcinoma and Gallbladder Cancer Studies","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Toronto General Hospital","funders":"Chugai Pharmaceutical; Sirtex Medical; Servier; Boston Scientific Corporation; European Cooperation in Science and Technology; Relay Therapeutics; Seagen; Pfizer; Incyte; Jazz Pharmaceuticals; Ipsen; BeiGene; Les Laboratories Pierre Fabre; FibroGen; Astellas Pharma; Eisai; Sanofi; Exelixis; Mylan; AstraZeneca; Novocure; Amgen; Eli Lilly and Company; Bristol-Myers Squibb","keywords":"Biliary tract cancer; Medicine; Precision oncology; Biliary tract; Precision medicine; Internal medicine; Oncology; Algorithm; Cancer; Pathology; Computer science; Gemcitabine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0102782,0.001306933,0.001648218,0.003417192,0.0009067367,0.003710754,0.003139386,0.003773011,0.006837429],"category_scores_gemma":[0.02091765,0.0004721619,0.002314656,0.001710049,0.001004946,0.003755587,0.003097791,0.004578869,0.003329568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003054895,"about_ca_system_score_gemma":0.01020424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003366176,"about_ca_topic_score_gemma":0.01013072,"domain_scores_codex":[0.9933692,0.003271076,0.001061036,0.0004696701,0.001538793,0.0002903009],"domain_scores_gemma":[0.9920893,0.003640107,0.0008624867,0.000180581,0.002809551,0.0004179756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001067161,0.00007280159,0.001732578,0.02549948,0.0003284047,0.0006525715,0.0005883058,0.0012559,0.0003008757,0.01250538,0.1457214,0.8112355],"study_design_scores_gemma":[0.0001842696,0.0001493363,0.00307947,0.1090719,0.001251549,0.003220999,0.001591284,0.002335369,0.0005839893,0.02400722,0.8543925,0.0001320504],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002132811,0.6936908,0.03810183,0.2239986,0.006244345,0.001461049,0.001058353,0.0008627257,0.03244947],"genre_scores_gemma":[0.05091146,0.6772915,0.19531,0.05883176,0.002806146,0.00182784,0.002241671,0.0001691267,0.01061047],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.0102782,"threshold_uncertainty_score":0.05435693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05559624531280107,"score_gpt":0.3928302502004648,"score_spread":0.3372340048876638,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}