{"id":"W2008413774","doi":"10.1158/1078-0432.mechres-ia11","title":"Abstract IA11: Using Molecular Profiling Strategies in Clinical Trials to Understand Resistance Mechanisms in the Era of Personalized Cancer Medicine","year":2012,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Melanoma and MAPK Pathways","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Vemurafenib; Medicine; Acquired resistance; Druggability; Drug resistance; Clinical trial; Melanoma; Oncology; Precision medicine; Cancer; Cancer research; Ipilimumab; Internal medicine; Bioinformatics; Biology; Metastatic melanoma; Pathology; Immunotherapy; Gene; Genetics","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":[0.03040442,0.001152672,0.002341282,0.001070833,0.0004272641,0.003961286,0.001065203,0.002089937,0.01053836],"category_scores_gemma":[0.01992282,0.0005399668,0.001552677,0.001415905,0.001350938,0.002629682,0.001336877,0.004212278,0.002579676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002228438,"about_ca_system_score_gemma":0.00336668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005979958,"about_ca_topic_score_gemma":0.001104467,"domain_scores_codex":[0.9831844,0.01338299,0.0007208866,0.0008436212,0.001213821,0.000654321],"domain_scores_gemma":[0.9827811,0.006083719,0.004662587,0.001715924,0.002760269,0.001996446],"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":[0.05027388,0.004496291,0.03342643,0.003200856,0.002335666,0.0001533743,0.0002887324,0.006459632,0.03531258,0.01289938,0.05106457,0.8000886],"study_design_scores_gemma":[0.05951281,0.2299921,0.1613565,0.004605367,0.006683446,0.001375741,0.0008154723,0.05153672,0.0396116,0.0448347,0.3989852,0.0006904171],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.4976553,0.1063288,0.1294905,0.1122408,0.008174869,0.02289899,0.01825424,0.004297121,0.1006594],"genre_scores_gemma":[0.7932592,0.02487186,0.1164673,0.03099059,0.002494844,0.01327987,0.006292419,0.0003404078,0.0120036],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03040442,"threshold_uncertainty_score":0.1607959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5070920791879917,"score_gpt":0.5937702580808246,"score_spread":0.08667817889283291,"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."}}