{"id":"W3156150685","doi":"10.2196/23161","title":"Data Integration to Improve Real-world Health Outcomes Research for Non–Small Cell Lung Cancer in the United States: Descriptive and Qualitative Exploration","year":2021,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Lung Cancer Treatments and Mutations","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eli Lilly and Company","keywords":"Medicine; Cohort; Descriptive statistics; Atezolizumab; Medical record; Nivolumab; Cancer; Internal medicine; Database; Statistics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0007480097,0.0001343164,0.0002472081,0.000252774,0.000176299,0.00006938798,0.0001089057,0.00003621674,0.00002757833],"category_scores_gemma":[0.0000933178,0.00008969669,0.00002597304,0.000910437,0.00004217745,0.0002255619,0.00006102462,0.0002038761,0.000001329344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001015628,"about_ca_system_score_gemma":0.001243065,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03234359,"about_ca_topic_score_gemma":0.1483656,"domain_scores_codex":[0.9984647,0.0002763816,0.0002790546,0.0004305791,0.0002542012,0.0002950726],"domain_scores_gemma":[0.9984727,0.0003865166,0.00008634347,0.0004074339,0.0005414328,0.0001055677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0008739728,0.000647591,0.01867996,0.0009288018,0.0003852306,0.0000173862,0.8425573,0.0002520576,0.001816998,0.001352367,0.09409925,0.03838915],"study_design_scores_gemma":[0.01022152,0.001809092,0.08767891,0.002077652,0.0005154489,0.000001672733,0.7996916,0.02096213,0.004152867,0.001572393,0.07070503,0.0006117332],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7826239,0.00297457,0.01074603,0.1779681,0.0007533562,0.01634897,0.007254803,0.00006754311,0.001262764],"genre_scores_gemma":[0.9443627,0.00524595,0.004292051,0.009093429,0.0003568805,0.01967318,0.005711856,0.00007717583,0.01118679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1688747,"threshold_uncertainty_score":0.9741001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2393260491004993,"score_gpt":0.5582879789472005,"score_spread":0.3189619298467011,"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."}}