{"id":"W3192284081","doi":"10.1016/j.ebiom.2021.103531","title":"Radiomics: The endocrinologists’ new best friend?","year":2021,"lang":"en","type":"letter","venue":"EBioMedicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Adipose tissue; Weight loss; Body mass index; Obesity; Radiomics; Internal medicine; Bioinformatics; Radiology; Biology","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":[0.007401791,0.001579128,0.002749101,0.003278075,0.001626073,0.005820807,0.002632531,0.009521456,0.00963211],"category_scores_gemma":[0.04532666,0.0005920037,0.001638218,0.001443911,0.00456516,0.01587952,0.002170324,0.02453246,0.007385822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001706045,"about_ca_system_score_gemma":0.002627491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002755258,"about_ca_topic_score_gemma":0.004301424,"domain_scores_codex":[0.9962415,0.001418907,0.0004581215,0.0005585139,0.001025534,0.0002974678],"domain_scores_gemma":[0.9644945,0.01682385,0.001604642,0.001234974,0.01140057,0.004441565],"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":[0.00008434514,0.0000314153,0.0008732396,0.0006393301,0.00005366041,0.0007089622,0.0004707171,0.000050187,0.0001786943,0.002211502,0.9271264,0.0675716],"study_design_scores_gemma":[0.00003224458,0.00007710981,0.0006774273,0.001955223,0.00006398623,0.00405763,0.001929511,0.0001423643,0.0001893262,0.008649572,0.9820983,0.0001273508],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0003747313,0.1441679,0.001181912,0.703046,0.1492836,0.00001141154,0.000155585,0.000165456,0.001613492],"genre_scores_gemma":[0.007535925,0.09984203,0.002694221,0.5049319,0.3761669,0.00005956363,0.0001113375,0.0002325417,0.008425528],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.00963211,"threshold_uncertainty_score":0.03914487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02713013240347573,"score_gpt":0.2985642889577194,"score_spread":0.2714341565542436,"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."}}