{"id":"W4360874226","doi":"10.1016/j.ejca.2023.03.024","title":"Molecular profiling and target actionability for precision medicine in neuroendocrine neoplasms: real-world data","year":2023,"lang":"en","type":"article","venue":"European Journal of Cancer","topic":"Neuroendocrine Tumor Research Advances","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Cilag; Chugai Pharmaceutical; Novartis Pharma; Genentech; Ipsen; PharmaMar; Eisai; Basilea Pharmaceutica; Daiichi Sankyo Europe; Servier; AstraZeneca; Novocure; Institut National Du Cancer; Eisai Canada; BeiGene; Agios Pharmaceuticals; Loxo Oncology; Astex Pharmaceuticals; Les Laboratories Pierre Fabre; Pfizer; Incyte; Boston Pharmaceuticals; Taiho Pharmaceutical; Bayer HealthCare; Sanofi; Exelixis; GlaxoSmithKline; Relay Therapeutics; Amgen; Celgene; Eli Lilly and Company; Bristol-Myers Squibb","keywords":"Neuroendocrine tumors; Medicine; MEN1; Everolimus; Internal medicine; Neuroendocrine carcinoma; PTEN; Oncology; Carcinoma; Endocrine system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01043973,0.0004230617,0.0006698112,0.001911781,0.0002921857,0.001928393,0.0007230886,0.0007929123,0.002112076],"category_scores_gemma":[0.02198626,0.0001727016,0.0007128803,0.003021838,0.0007680235,0.001492535,0.0008785165,0.0007046853,0.0005245221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009539488,"about_ca_system_score_gemma":0.0005408399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007018567,"about_ca_topic_score_gemma":0.0008591679,"domain_scores_codex":[0.9944782,0.002199232,0.0008526392,0.0008155081,0.001466357,0.0001880113],"domain_scores_gemma":[0.9531367,0.02398867,0.01509446,0.003653527,0.003472989,0.0006536112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001583293,0.00007876273,0.9602219,0.0003348286,0.0004385627,0.0001583186,0.00009614073,0.002745775,0.000896889,0.0003098504,0.0008460737,0.03228975],"study_design_scores_gemma":[0.00008935489,0.001097039,0.9712918,0.0002331932,0.0006671876,0.001892421,0.0002345574,0.007356235,0.003749705,0.001527583,0.01180583,0.00005509345],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9542815,0.0156828,0.007975106,0.001256908,0.00005470118,0.0002103087,0.01303565,0.0001165059,0.007386527],"genre_scores_gemma":[0.9919665,0.0009995562,0.002453415,0.0001258428,0.00004926594,0.0000845919,0.004159568,0.00001374626,0.0001474158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01043973,"threshold_uncertainty_score":0.05521119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07200199533469943,"score_gpt":0.4166217661205312,"score_spread":0.3446197707858318,"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."}}