{"id":"W4413473118","doi":"10.1007/s00330-025-11972-9","title":"Utility of machine learning for predicting severe chronic thromboembolic pulmonary hypertension based on CT metrics in a surgical cohort","year":2025,"lang":"en","type":"article","venue":"European Radiology","topic":"Pulmonary Hypertension Research and Treatments","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; St. Michael's Hospital; Toronto General Hospital; University of Toronto","funders":"","keywords":"Medicine; Pulmonary embolism; Pulmonary artery; Pulmonary hypertension; Logistic regression; Ventricle; Cardiology; Internal medicine; Neuroradiology; Cohort; Chronic thromboembolic pulmonary hypertension; Radiology; Pulmonary thromboendarterectomy","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":[0.004282694,0.0005744586,0.0006408008,0.001513784,0.0003966718,0.001646197,0.0006309102,0.0007189137,0.001245293],"category_scores_gemma":[0.01666909,0.0001965901,0.0007855844,0.0006724435,0.0004992394,0.001072194,0.0007096244,0.001058342,0.0004126492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004766999,"about_ca_system_score_gemma":0.0007863767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003034298,"about_ca_topic_score_gemma":0.002700359,"domain_scores_codex":[0.9988121,0.0005076605,0.0001176276,0.0002457917,0.0001862357,0.0001305737],"domain_scores_gemma":[0.9910243,0.005363951,0.001141015,0.0008343317,0.0008512399,0.0007852549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003626854,0.00008885204,0.9898937,0.000009198114,0.0001529084,0.00005887638,0.0000346846,0.001684973,0.0002268602,0.0000838502,0.0003128737,0.00709054],"study_design_scores_gemma":[0.00005274285,0.0009840365,0.8772392,0.0000319401,0.0002941432,0.00079815,0.0003510072,0.1177693,0.0008192702,0.001100789,0.0005193864,0.00003991138],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975548,0.0002466764,0.001031309,0.0002474294,0.00002444449,0.00001072598,0.0004335433,0.00002319155,0.0004278908],"genre_scores_gemma":[0.9986508,0.00008065822,0.0004980021,0.00002808551,0.00003373631,0.000006418524,0.0005564324,0.000007363971,0.0001386248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004282694,"threshold_uncertainty_score":0.02264935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02832412672975239,"score_gpt":0.292787060828625,"score_spread":0.2644629340988726,"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."}}