{"id":"W2942904736","doi":"10.14797/mdcj-14-3-188","title":"Use of Computed Tomography and Magnetic Resonance Imaging in Central Venous Disease","year":2018,"lang":"en","type":"review","venue":"Methodist DeBakey Cardiovascular Journal","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Thomas Hospital","funders":"British Heart Foundation","keywords":"Medicine; Radiology; Magnetic resonance imaging; Modality (human–computer interaction); Venous thrombosis; Thrombus; Pelvis; Computed tomography; Deep vein; Thrombosis; Surgery; Artificial intelligence","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"],"consensus_categories":[],"category_scores_codex":[0.002215049,0.0005580769,0.004202634,0.0008162144,0.0001345002,0.0001422664,0.0002467552,0.0001466814,0.00003196721],"category_scores_gemma":[0.0003665943,0.0004592509,0.003269271,0.0006823023,0.0002694448,0.0001335131,0.0001885613,0.0006982408,0.00000362818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001953107,"about_ca_system_score_gemma":0.0003441217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009622537,"about_ca_topic_score_gemma":0.000005003525,"domain_scores_codex":[0.995293,0.001453037,0.001146931,0.000646289,0.0007778984,0.0006828655],"domain_scores_gemma":[0.9977137,0.0002099857,0.0003625261,0.0009346754,0.0001970916,0.0005819912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003505067,0.0003606167,0.001119501,0.007897894,0.001087968,0.002105756,0.00008702157,0.00001118431,1.641762e-7,0.00002563335,0.0009144696,0.9863548],"study_design_scores_gemma":[0.0008722061,0.000138367,0.04523589,0.01913899,0.01058305,0.001274336,0.00001427392,0.00005778873,5.964601e-7,0.00004078294,0.9222565,0.0003872061],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001365588,0.9955149,0.002447095,0.00003173876,0.0005641897,0.001138643,0.00004786331,0.00002471863,0.00009424822],"genre_scores_gemma":[0.0001396659,0.9860674,0.01300977,0.00008210962,0.0005065508,0.00004096182,0.00003879531,0.00009386228,0.0000209136],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9859675,"threshold_uncertainty_score":0.9997859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04952769164464595,"score_gpt":0.3068658574889795,"score_spread":0.2573381658443336,"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."}}