{"id":"W4380576433","doi":"10.1186/s12885-023-10997-x","title":"Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study","year":2023,"lang":"en","type":"article","venue":"BMC Cancer","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Hospital Foundation","funders":"","keywords":"Precision medicine; Context (archaeology); Computer science; Omics; Surgical oncology; Data science; Identification (biology); Medicine; Machine learning; Artificial intelligence; Data mining; Bioinformatics; Pathology; Internal medicine","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":[0.01993153,0.000515148,0.0003558333,0.0005665721,0.0005331341,0.00247397,0.001127677,0.0008609807,0.009703903],"category_scores_gemma":[0.02606409,0.0002456717,0.00151739,0.0004633061,0.001480458,0.002070789,0.004200237,0.001396829,0.001076879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012514,"about_ca_system_score_gemma":0.001421192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007862729,"about_ca_topic_score_gemma":0.001311708,"domain_scores_codex":[0.9890869,0.009838833,0.000136047,0.0003750035,0.0004438155,0.0001194526],"domain_scores_gemma":[0.9832153,0.0113562,0.0008362972,0.002900443,0.0008097126,0.0008820769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007131144,0.004207644,0.08089246,0.002252689,0.001795879,0.0006962683,0.01067496,0.01625019,0.004953742,0.0853733,0.07048516,0.7152866],"study_design_scores_gemma":[0.006202953,0.01795733,0.04884051,0.003250628,0.00283632,0.002567609,0.01308585,0.09935389,0.01095914,0.2270811,0.567368,0.0004966491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6926587,0.006715129,0.2006152,0.04848202,0.001611103,0.003502971,0.004900652,0.00158456,0.03992977],"genre_scores_gemma":[0.6686055,0.002629061,0.3062456,0.005660104,0.0006075301,0.00525229,0.002264221,0.0003282706,0.008407531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01993153,"threshold_uncertainty_score":0.1054093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1174665885050456,"score_gpt":0.5249293148443741,"score_spread":0.4074627263393285,"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."}}