{"id":"W2288152566","doi":"10.1200/jop.2015.009316","title":"Reply to F. Dayyani et al","year":2016,"lang":"en","type":"letter","venue":"Journal of Oncology Practice","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Columbia College","funders":"","keywords":"Medicine; Biomarker; Disease; Prospective cohort study; Internal medicine; Oncology","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":[0.002388978,0.0007809324,0.00125277,0.0007484515,0.00221629,0.00208412,0.0016798,0.02627619,0.005733589],"category_scores_gemma":[0.02238371,0.0006208363,0.000841159,0.0006239857,0.001980706,0.00377706,0.001119483,0.02636897,0.00594328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002348062,"about_ca_system_score_gemma":0.002211938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002421848,"about_ca_topic_score_gemma":0.003047311,"domain_scores_codex":[0.9982521,0.0004834632,0.000237389,0.0002961643,0.0004975519,0.0002332713],"domain_scores_gemma":[0.9950508,0.001690705,0.0004521836,0.0001644476,0.001636597,0.001005182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002983793,0.00001349901,0.000403722,0.00003413524,0.000006641678,0.001165147,0.00006113139,0.00002209265,0.00005372481,0.0003740104,0.9942033,0.003632867],"study_design_scores_gemma":[0.000108405,0.00007425664,0.002177094,0.0003530554,0.00003761615,0.01262308,0.0007375213,0.0005323197,0.0004083921,0.004400117,0.9784209,0.0001272372],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0004590291,0.001978421,0.00009840867,0.9628677,0.03328733,0.00001154037,0.00006114943,0.00004810563,0.001188366],"genre_scores_gemma":[0.005504325,0.001639257,0.0002288626,0.9383801,0.04872317,0.00003524208,0.00004468458,0.00003028323,0.005413975],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02627619,"threshold_uncertainty_score":0.01918072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02566821281620939,"score_gpt":0.4083224956143079,"score_spread":0.3826542827980985,"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."}}