{"id":"W3025639393","doi":"10.1002/mp.14224","title":"Comparison of robust to standardized CT radiomics models to predict overall survival for non‐small cell lung cancer patients","year":2020,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Radiomics; Lung cancer; Medicine; Computed tomography; Cancer; Medical imaging; Radiology; Medical physics; Nuclear medicine; Oncology; 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":[0.0003870578,0.0002573549,0.001007345,0.00004879376,0.00007332419,0.00001999639,0.0003245168,0.00008586587,0.00005924335],"category_scores_gemma":[0.0006954917,0.0002292291,0.0002164027,0.0002740913,0.00009270146,0.00005792963,0.0001586282,0.0005501172,0.000004053257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001404535,"about_ca_system_score_gemma":0.0004324729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001016926,"about_ca_topic_score_gemma":0.000004160421,"domain_scores_codex":[0.9972278,0.00004802001,0.000624036,0.000468139,0.001161659,0.0004703596],"domain_scores_gemma":[0.9977446,0.0002263934,0.0001574014,0.0002454012,0.0002621621,0.001364019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004691993,0.004229783,0.3011945,0.005218354,0.0009169074,0.0000601337,0.01510009,0.283026,0.004653914,0.0006062335,0.2677713,0.1125308],"study_design_scores_gemma":[0.008737142,0.001017579,0.00131388,0.0005075616,0.0003242299,6.704805e-7,0.00006733721,0.9763712,0.005860083,0.0001442057,0.0053173,0.0003387815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4232216,0.00006392078,0.570206,0.004466948,0.0004243134,0.001010249,0.0001129874,0.00004738942,0.0004465316],"genre_scores_gemma":[0.9851428,0.0000341636,0.007484182,0.005948572,0.001045983,0.00008196082,0.0001192489,0.00007734485,0.00006573799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6933452,"threshold_uncertainty_score":0.934769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0326943239640338,"score_gpt":0.3225880767256473,"score_spread":0.2898937527616135,"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."}}