{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002192745,0.0002429439,0.0004298975,0.00005447468,0.0002367313,0.00002946991,0.0002023452,0.00009381976,0.0001287347],"category_scores_gemma":[0.0000336165,0.0001472194,0.00009011167,0.0005333962,0.0003727356,0.0001239628,0.0003115922,0.0002824431,0.000003129492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002419746,"about_ca_system_score_gemma":0.0004316952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006263147,"about_ca_topic_score_gemma":0.0002414751,"domain_scores_codex":[0.9983181,0.00004696361,0.0002810885,0.0004283649,0.0004563006,0.0004691757],"domain_scores_gemma":[0.9990216,0.0001893814,0.0001545947,0.00038052,0.000141011,0.0001128608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001877174,0.001102732,0.174149,0.0436742,0.002876175,0.0002455283,0.2665206,0.001649055,0.3021271,0.002168299,0.1326171,0.07099296],"study_design_scores_gemma":[0.01110867,0.0008390708,0.05308234,0.004545623,0.001430966,0.00002215857,0.02672482,0.01932774,0.7192965,0.0003848099,0.161937,0.001300308],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7583649,0.2174509,0.00005618666,0.003120354,0.0004862027,0.001542434,0.00005138519,0.0001638839,0.01876371],"genre_scores_gemma":[0.9606317,0.0339521,0.0004423262,0.0002370996,0.0003531084,0.0007753798,0.00001279166,0.00005314101,0.003542369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4171694,"threshold_uncertainty_score":0.6003432,"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."}}