{"id":"W3047861159","doi":"10.21037/tlcr-19-577","title":"Radiomics nomogram for prediction disease-free survival and adjuvant chemotherapy benefits in patients with resected stage I lung adenocarcinoma","year":2020,"lang":"en","type":"article","venue":"Translational Lung Cancer Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; University of Manitoba","funders":"Shanghai Pulmonary Hospital; Natural Science Foundation of Shanghai","keywords":"Nomogram; Medicine; Radiomics; Radiogenomics; Stage (stratigraphy); Proportional hazards model; Oncology; Adenocarcinoma; Internal medicine; Lung cancer; Radiology; Cohort; Lasso (programming language); Cancer","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":[0.0005447579,0.0001479623,0.0002745898,0.0001921456,0.0001420912,0.00004229334,0.0001439131,0.00006689012,0.00006656443],"category_scores_gemma":[0.0001986065,0.0001272209,0.00004860116,0.0004442174,0.0001537579,0.000121212,0.00002342649,0.0005765279,3.167913e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001360856,"about_ca_system_score_gemma":0.0003757346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001338451,"about_ca_topic_score_gemma":0.00005440904,"domain_scores_codex":[0.9978284,0.00009346124,0.0002661041,0.000445156,0.0009696322,0.0003972831],"domain_scores_gemma":[0.998844,0.0002385056,0.00004101133,0.0001828293,0.0002836039,0.0004100037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007083228,0.000113023,0.9798477,0.0004997423,0.0001006626,0.000007640476,0.0005422641,0.001699815,0.0001152018,0.0004553037,0.00056534,0.008970058],"study_design_scores_gemma":[0.01073462,0.0002991987,0.787238,0.0002264608,0.00004186479,5.034742e-7,0.00002064793,0.2006924,0.00001745263,0.000045662,0.0005828854,0.000100258],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682512,0.004266072,0.002286229,0.02250757,0.00006475855,0.001917912,0.000571768,0.0000491176,0.00008540184],"genre_scores_gemma":[0.9971911,0.00032563,0.00115439,0.0002689067,0.0003117246,0.000173927,0.0004440338,0.00005627697,0.00007403291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1989926,"threshold_uncertainty_score":0.518792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03250252776826584,"score_gpt":0.3478660685495756,"score_spread":0.3153635407813098,"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."}}