{"id":"W4405363665","doi":"10.1101/2024.02.22.24302979","title":"Determining Line of Therapy from Real-World Data in Non-Small Cell Lung Cancer","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Lung Cancer Treatments and Mutations","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"Sanofi Genzyme; Genentech; Shionogi; Regeneron Pharmaceuticals; Natera; MacroGenics; Pfizer; Incyte; BeiGene; Mirati Therapeutics; AstraZeneca; Novocure; Eisai; Jazz Pharmaceuticals; Puma Biotechnology; Daiichi Sankyo Europe; Gilead Sciences; Sanofi; Celgene; Eli Lilly and Company; Bristol-Myers Squibb; Seagen; LUNGevity Foundation; Amgen; Johns Hopkins University; Mesothelioma Applied Research Foundation","keywords":"Lung cancer; Line (geometry); Medicine; Oncology; Mathematics; Geometry","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":[0.03354816,0.0006921633,0.001498576,0.004922674,0.0008337839,0.003742369,0.002446471,0.0009529641,0.0009486813],"category_scores_gemma":[0.1135022,0.0004652696,0.001863581,0.003480338,0.0006854453,0.001915914,0.00142714,0.001554738,0.0004070131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001703205,"about_ca_system_score_gemma":0.004184298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006048038,"about_ca_topic_score_gemma":0.008986227,"domain_scores_codex":[0.9660099,0.01411519,0.008577733,0.004355655,0.006417332,0.0005241043],"domain_scores_gemma":[0.777509,0.1784344,0.02023897,0.008581133,0.01383074,0.001405678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001257424,0.0003823655,0.5629291,0.00312322,0.001899529,0.000947199,0.0009686865,0.1053325,0.002484057,0.004543686,0.02545043,0.2906817],"study_design_scores_gemma":[0.0005350531,0.0008808927,0.213417,0.004569297,0.001847375,0.002928762,0.001864321,0.6080906,0.02343342,0.04112044,0.1008841,0.0004288449],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.51388,0.009283154,0.406678,0.006597174,0.000491514,0.003363184,0.04147036,0.002686955,0.01554982],"genre_scores_gemma":[0.7508851,0.001164594,0.2260464,0.0009471255,0.0001836728,0.001024657,0.01911423,0.0001382359,0.0004959524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03354816,"threshold_uncertainty_score":0.1774217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05815704656506881,"score_gpt":0.393274180367731,"score_spread":0.3351171338026622,"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."}}