{"id":"W4281825562","doi":"10.1200/jco.2022.40.16_suppl.9128","title":"Molecular testing and patterns of treatment in patients with NSCLC: An IASLC analysis of ASCO CancerLinQ Discovery Data.","year":2022,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"International Association for the Study of Lung Cancer","keywords":"Medicine; Internal medicine; Lung cancer; Oncology; Adenocarcinoma; Stage (stratigraphy); Univariate analysis; Logistic regression; Cancer; Multivariate analysis","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":[0.007934559,0.0003506901,0.001085052,0.002883913,0.0005055387,0.001333217,0.00109051,0.0006423616,0.002552649],"category_scores_gemma":[0.02542257,0.0003311283,0.001428374,0.007163721,0.0003361858,0.0006508196,0.001617752,0.0009849914,0.0006935232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001674034,"about_ca_system_score_gemma":0.002629153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02720231,"about_ca_topic_score_gemma":0.0275266,"domain_scores_codex":[0.9935883,0.001931638,0.0008800835,0.001569775,0.001391352,0.0006387771],"domain_scores_gemma":[0.9756528,0.01166977,0.007937486,0.002104292,0.001758863,0.0008767288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005288741,0.00002794694,0.986459,0.0001842213,0.0004664554,0.0001177064,0.00006226177,0.0005493251,0.0001353524,0.0001099701,0.007493062,0.00386596],"study_design_scores_gemma":[0.0001787104,0.0001266499,0.9787493,0.0001703955,0.0005059545,0.0008835961,0.000259612,0.007229991,0.0002183411,0.0002941852,0.01135352,0.00002981818],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7681476,0.005120627,0.001645549,0.002125338,0.00006380007,0.0002054176,0.2203223,0.0001608144,0.002208473],"genre_scores_gemma":[0.8591763,0.0006412611,0.001881375,0.0005213784,0.00004526905,0.0002012924,0.1370918,0.0000551323,0.0003862495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02720231,"threshold_uncertainty_score":0.05408794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07360579011254115,"score_gpt":0.4469978150138076,"score_spread":0.3733920249012664,"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."}}