{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001228191,0.0001508762,0.0004148416,0.000512693,0.00004485372,0.00001723835,0.000310704,0.000001871855,0.00004000241],"category_scores_gemma":[0.002416161,0.000113581,0.00004894099,0.0006725253,0.000156519,0.0002700751,0.000232803,0.0004096511,0.000003924754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007452808,"about_ca_system_score_gemma":0.000145817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004677979,"about_ca_topic_score_gemma":0.00009847624,"domain_scores_codex":[0.9978008,0.0003069467,0.0006332996,0.000347664,0.00056483,0.0003464397],"domain_scores_gemma":[0.9983919,0.0003776565,0.000279579,0.0004332504,0.0002812613,0.0002363804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01386599,0.0004467001,0.434975,0.001852146,0.0003505145,0.07191681,0.0003026331,0.00418241,0.1694992,0.0003376955,0.06011253,0.2421584],"study_design_scores_gemma":[0.01618608,0.004908935,0.557813,0.001712318,0.0002246643,0.003202613,0.0004437929,0.006672904,0.04394578,0.001129877,0.3633149,0.0004450953],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788863,0.001150395,0.0001622971,0.01746128,0.0004026542,0.000627784,0.00004073999,0.00004031466,0.001228202],"genre_scores_gemma":[0.9908548,0.004052443,0.003015493,0.0003532838,0.0006561131,0.00001269587,0.00003448686,0.0000775725,0.0009431021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3032024,"threshold_uncertainty_score":0.4631698,"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."}}