{"id":"W3110943061","doi":"10.1183/23120541.00461-2020","title":"Importance of computed tomography in defining segmental disease in chronic thromboembolic pulmonary hypertension","year":2020,"lang":"en","type":"article","venue":"ERJ Open Research","topic":"Pulmonary Hypertension Research and Treatments","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Pulmonary artery; Pulmonary hypertension; Pulmonary angiography; Cardiology; Radiology; Internal medicine; Chronic thromboembolic pulmonary hypertension","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":[],"consensus_categories":[],"category_scores_codex":[0.0007669252,0.0001795558,0.0006089318,0.0005391049,0.00009224965,0.00003708474,0.0004998918,0.00006911904,0.0002877685],"category_scores_gemma":[0.0002560052,0.0001503551,0.00009642801,0.002134593,0.000215081,0.0002391759,0.0008826184,0.0006953956,0.0001073267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002498936,"about_ca_system_score_gemma":0.0008430199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001224227,"about_ca_topic_score_gemma":0.0001423208,"domain_scores_codex":[0.9968144,0.0003370967,0.0004506054,0.0006377382,0.001024067,0.0007361195],"domain_scores_gemma":[0.998427,0.000193962,0.00005076207,0.0004512332,0.0001802948,0.0006967828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002118428,0.001523568,0.9531807,0.0002404021,0.0000559865,0.0189077,0.00008334471,0.00003351193,0.01796731,0.00009300518,0.00128039,0.004515633],"study_design_scores_gemma":[0.003570804,0.0007731286,0.9769683,0.0005864286,0.00001095026,0.00004654075,0.0001879513,0.01642038,0.0008664838,0.0001816167,0.0002577208,0.0001296344],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988107,0.004034064,0.000001179367,0.003385643,0.00001905555,0.001829704,0.00003567575,0.00001876194,0.002568878],"genre_scores_gemma":[0.9982812,0.0004087288,0.0004650242,0.0004199783,0.00003767401,0.0001165891,0.0001931265,0.0000314475,0.00004621772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02378764,"threshold_uncertainty_score":0.6131304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1113869339524323,"score_gpt":0.3830152044787408,"score_spread":0.2716282705263086,"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."}}