{"id":"W1193864810","doi":"10.1118/1.4925763","title":"TU‐G‐303‐03: Machine Learning to Improve Human Learning From Longitudinal Image Sets","year":2015,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Radiomics; Radiogenomics; Medical imaging; Machine learning; Artificial intelligence; Computer science; Medical physics; Precision medicine; Medicine; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008132124,0.001983494,0.001702531,0.002019276,0.0008260931,0.002570499,0.003824689,0.003540228,0.01408344],"category_scores_gemma":[0.01754086,0.0008753676,0.002333027,0.002572327,0.001109459,0.002711589,0.003249486,0.004254773,0.009367291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001474123,"about_ca_system_score_gemma":0.001653615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007262104,"about_ca_topic_score_gemma":0.00824824,"domain_scores_codex":[0.9968836,0.0015025,0.0001325243,0.0007657254,0.000540963,0.0001747657],"domain_scores_gemma":[0.9926471,0.003627966,0.0002128582,0.001988955,0.001281173,0.0002419598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002236063,0.0003209563,0.001900908,0.000236033,0.0003448205,0.00007847539,0.0001425276,0.08239025,0.002305474,0.01303816,0.1104595,0.7885591],"study_design_scores_gemma":[0.00004589754,0.0001173029,0.00101343,0.00005850639,0.00003647373,0.00005977138,0.00003526568,0.9469395,0.002336146,0.02681119,0.02251511,0.0000313886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009491961,0.002674348,0.9513033,0.002941057,0.001070268,0.0002490021,0.002429797,0.02240917,0.007431029],"genre_scores_gemma":[0.1228283,0.00214305,0.825601,0.001901814,0.001260554,0.001026359,0.01390989,0.003960049,0.027369],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01408344,"threshold_uncertainty_score":0.04711384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02386549629881744,"score_gpt":0.321071521055217,"score_spread":0.2972060247563996,"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."}}