{"id":"W4386600587","doi":"10.1016/j.ccell.2023.08.007","title":"Unfolding the secrets of small cell lung cancer progression: Novel approaches and insights through rapid autopsies","year":2023,"lang":"en","type":"article","venue":"Cancer Cell","topic":"Lung Cancer Research Studies","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Cancer Moonshot; National Cancer Institute; National Research, Development and Innovation Office; University of Texas MD Anderson Cancer Center; National Institutes of Health; International Association for the Study of Lung Cancer; Hungarian-American Enterprise Scholarship Fund; Fru Berta Kamprads Stiftelse; Sierra Oncology; Innovációs és Technológiai Minisztérium; Magyar Tudományos Akadémia; BeiGene; Magyar Tüdőgyógyász Társaság; Cancer Prevention and Research Institute of Texas; Austrian Science Fund; Moonshot Research and Development Program; AstraZeneca; Bristol-Myers Squibb","keywords":"Biology; Lung cancer; Cancer; Cancer research; Cell; Computational biology; Medicine; Pathology; Genetics","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":[0.00085351,0.0004083684,0.0003904252,0.000495934,0.0006582641,0.001448393,0.0004754336,0.0006216232,0.00304613],"category_scores_gemma":[0.001132167,0.000252519,0.0004655678,0.0002723895,0.001339125,0.00307804,0.001560031,0.002386328,0.000411252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000783693,"about_ca_system_score_gemma":0.0005032401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003651036,"about_ca_topic_score_gemma":0.0005407187,"domain_scores_codex":[0.9997054,0.00004596672,0.00001241089,0.00007129541,0.00009538907,0.00006956176],"domain_scores_gemma":[0.9994255,0.0001909163,0.0001117334,0.0001311367,0.00005882383,0.00008183221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007944951,0.0001964635,0.005153226,0.0007357345,0.0001260239,0.001276096,0.001304545,0.003807996,0.7519874,0.1187651,0.003607966,0.1122449],"study_design_scores_gemma":[0.0001374772,0.0009985009,0.02053499,0.0002692703,0.0001718469,0.003140708,0.002335296,0.03242224,0.6873217,0.1629101,0.08959574,0.0001621477],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8179442,0.05508159,0.0900941,0.01003768,0.001516162,0.000190644,0.0006232802,0.000548711,0.02396373],"genre_scores_gemma":[0.9709561,0.0112207,0.01150995,0.0006854137,0.0002717779,0.0000710334,0.000156183,0.00006304864,0.005065778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00304613,"threshold_uncertainty_score":0.01019031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09589570687595671,"score_gpt":0.3582686172030531,"score_spread":0.2623729103270964,"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."}}