{"id":"W4307995537","doi":"10.1007/s00259-022-06001-6","title":"Joint EANM/SNMMI guideline on radiomics in nuclear medicine","year":2022,"lang":"en","type":"letter","venue":"European Journal of Nuclear Medicine and Molecular Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Universitätsklinikum Essen; National Cancer Institute; Society of Nuclear Medicine and Molecular Imaging","keywords":"Radiomics; Guideline; Medical physics; Medicine; Computer science; Radiology; Pathology","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":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004775106,0.0007011511,0.001835307,0.00179932,0.0001912293,0.00005161588,0.0005834518,0.0001272365,0.001416204],"category_scores_gemma":[0.002199407,0.0005450988,0.0003418878,0.0004427154,0.0007093148,0.000108104,0.0002982136,0.00796758,0.00002455465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002893687,"about_ca_system_score_gemma":0.0001203823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005506079,"about_ca_topic_score_gemma":1.309262e-7,"domain_scores_codex":[0.9935327,0.001135503,0.002315023,0.000698085,0.001620279,0.0006984791],"domain_scores_gemma":[0.9970636,0.0001823749,0.001315507,0.0006919528,0.0002562468,0.0004903658],"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":[0.0002190162,0.00005865334,0.00004791609,0.0002008737,0.0001733645,0.07831584,0.001347657,0.00004299855,0.00508986,0.00007206949,0.9038738,0.01055796],"study_design_scores_gemma":[0.006310401,0.002130528,0.000143602,0.003387073,0.0005537932,0.008476072,0.001046061,0.002514857,0.000005882946,0.00006654013,0.9749423,0.0004228986],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.003506632,0.003640678,0.0002975036,0.9668044,0.001264589,0.0002933001,0.000004321768,0.00006115245,0.02412738],"genre_scores_gemma":[0.0119914,0.001975556,0.002598558,0.9761559,0.006577217,8.283558e-7,0.00009061949,0.0004914833,0.0001183897],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.0710685,"threshold_uncertainty_score":0.9997001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779605959948308,"score_gpt":0.2732405385382184,"score_spread":0.2554444789387353,"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."}}