{"id":"W2981959691","doi":"10.1038/s41597-019-0241-0","title":"Distributed radiomics as a signature validation study using the Personal Health Train infrastructure","year":2019,"lang":"en","type":"article","venue":"Scientific Data","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Radiomics; Computer science; Signature (topology); Data mining; Artificial intelligence; Mathematics","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":[0.004782436,0.0005518024,0.0006464093,0.000782558,0.0003322199,0.000998722,0.001383253,0.001006087,0.001061023],"category_scores_gemma":[0.009878643,0.0001781675,0.0005593488,0.0007615177,0.0006560026,0.0009651993,0.001165426,0.0008028027,0.0004764557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009671117,"about_ca_system_score_gemma":0.001071281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005429755,"about_ca_topic_score_gemma":0.003122821,"domain_scores_codex":[0.9971694,0.001287698,0.0001423834,0.0006853271,0.0004926866,0.0002224511],"domain_scores_gemma":[0.9937122,0.002044102,0.0007231584,0.002123839,0.0009596382,0.0004368899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002627222,0.00179253,0.1712368,0.0003518127,0.0006498237,0.001125585,0.0005048535,0.6067833,0.01513575,0.007332141,0.01258326,0.179877],"study_design_scores_gemma":[0.0002617003,0.001139743,0.04127233,0.00003526935,0.0001021128,0.0006154444,0.0003182021,0.9363297,0.01117538,0.003353873,0.005342338,0.00005386578],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8982314,0.0002741723,0.09508477,0.0006568157,0.0001601096,0.0003519548,0.001676586,0.001228977,0.002335178],"genre_scores_gemma":[0.9818032,0.00003580546,0.0158056,0.00007236186,0.00003801612,0.00007989236,0.001605126,0.00002929657,0.0005306557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005429755,"threshold_uncertainty_score":0.02529228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03081040018237908,"score_gpt":0.3496671711163765,"score_spread":0.3188567709339974,"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."}}