{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008971595,0.0002391207,0.0004940139,0.00007971049,0.0002532435,0.00006823699,0.0001935944,0.00008644349,0.00009058022],"category_scores_gemma":[0.00110957,0.0001328581,0.0001746308,0.0002851394,0.0001119823,0.0001782241,0.0001260108,0.0002713899,0.00001583048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005755962,"about_ca_system_score_gemma":0.0003136925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004248281,"about_ca_topic_score_gemma":0.009851431,"domain_scores_codex":[0.9979739,0.00007739357,0.0006668236,0.0005363516,0.0004327628,0.0003127535],"domain_scores_gemma":[0.9974112,0.00142833,0.000271235,0.0004758502,0.0002782059,0.0001351854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005771082,0.0007863516,0.8748369,0.0001681708,0.0006764083,0.00002714616,0.004202301,0.00005589965,0.00004308344,0.0000425097,0.05720309,0.06138109],"study_design_scores_gemma":[0.03163595,0.004112156,0.7644133,0.003516173,0.003891262,0.00002008016,0.0196492,0.01561281,0.0002311975,0.0001313252,0.1561086,0.000677969],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9763004,0.004434015,0.0007456891,0.01083567,0.002304155,0.004617254,0.0001569184,0.0002022117,0.0004036413],"genre_scores_gemma":[0.9891865,0.001581928,0.0007470927,0.001070106,0.002059909,0.002782241,0.0001222066,0.00007092457,0.002379097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1104236,"threshold_uncertainty_score":0.6422158,"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."}}