{"id":"W2807220353","doi":"10.2967/jnumed.118.209007","title":"Feasibility of <sup>18</sup>F-FDG Dose Reductions in Breast Cancer PET/MRI","year":2018,"lang":"en","type":"article","venue":"Journal of Nuclear Medicine","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"Universitätsspital Zürich","keywords":"Wilcoxon signed-rank test; Nuclear medicine; Medicine; Breast cancer; Image quality; Image noise; Mann–Whitney U test; Iterative reconstruction; Lesion; Cancer; Mathematics; Radiology; Pathology; Artificial intelligence; Computer science; Image (mathematics); Internal medicine","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":[0.001392759,0.000318553,0.0002952644,0.000209559,0.0001250957,0.0005453608,0.0003134311,0.0005108551,0.0007769649],"category_scores_gemma":[0.0043652,0.0001733986,0.0002709494,0.0001125067,0.000289488,0.0002290236,0.0001851396,0.000303411,0.0002616639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002436328,"about_ca_system_score_gemma":0.0002314486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003308913,"about_ca_topic_score_gemma":0.0003990854,"domain_scores_codex":[0.999028,0.0006212604,0.00004491832,0.0000777067,0.0001827948,0.00004536228],"domain_scores_gemma":[0.9991793,0.0005095145,0.0001354965,0.00005921442,0.00008772942,0.00002857944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01126047,0.0007007855,0.267787,0.000569639,0.0002577999,0.001537533,0.0006612993,0.01673479,0.4976459,0.0003823384,0.0006492553,0.2018131],"study_design_scores_gemma":[0.0003551161,0.0150875,0.6076168,0.00007653618,0.0003958219,0.01104756,0.0006425378,0.03199865,0.3253084,0.0004920886,0.006900973,0.00007807721],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904673,0.001473041,0.006937055,0.0001575813,0.00000663664,0.00003033288,0.00004832319,0.00006257494,0.0008172211],"genre_scores_gemma":[0.9928059,0.0002569696,0.006569105,0.00005633579,0.00001531563,0.00002306237,0.00009566823,0.00002320945,0.0001545199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001392759,"threshold_uncertainty_score":0.007365644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05008313765789246,"score_gpt":0.3842750096203099,"score_spread":0.3341918719624174,"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."}}