{"id":"W4390235690","doi":"10.1002/art.42791","title":"Performance of DETECT Pulmonary Arterial Hypertension Algorithm According to the Hemodynamic Definition of Pulmonary Arterial Hypertension in the 2022 European Society of Cardiology and the European Respiratory Society Guidelines","year":2023,"lang":"en","type":"article","venue":"Arthritis & Rheumatology","topic":"Pulmonary Hypertension Research and Treatments","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Actelion Pharmaceuticals; Chugai Pharmaceutical; Idorsia Pharmaceuticals; Bristol-Myers Squibb; AstraZeneca; CSL Behring; Sanofi; Genentech; Alnylam Pharmaceuticals; Acceleron; United Therapeutics Corporation; Servier; Pfizer","keywords":"Medicine; Right heart catheterization; Internal medicine; Pulmonary wedge pressure; Pulmonary hypertension; Hemodynamics; Cardiology; Pulmonary arterial pressure; Vascular resistance; Chronic thromboembolic pulmonary hypertension; Algorithm; Computer science","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":[0.00804874,0.0006105877,0.001501987,0.001419089,0.0003304387,0.00160969,0.0007364345,0.001220163,0.000981675],"category_scores_gemma":[0.01634566,0.0003003591,0.001650132,0.0007645583,0.0002220267,0.0006166744,0.0009639362,0.0006551385,0.0004406249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001054471,"about_ca_system_score_gemma":0.002220259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003477884,"about_ca_topic_score_gemma":0.006196994,"domain_scores_codex":[0.9901677,0.004976355,0.001475589,0.0009055392,0.00204026,0.0004344821],"domain_scores_gemma":[0.9921687,0.002266735,0.001728659,0.0002972175,0.003090549,0.000448028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003128886,0.0007862386,0.8942156,0.0007085081,0.0015201,0.0001846555,0.0001919999,0.003986942,0.001133865,0.00048901,0.02028823,0.07336602],"study_design_scores_gemma":[0.00112833,0.002175933,0.9514359,0.0005982034,0.001417114,0.001194525,0.0002285551,0.02480154,0.001796359,0.0006787291,0.01444392,0.0001008559],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9399911,0.009326412,0.01313327,0.003743834,0.0005774839,0.003110333,0.01082652,0.0003661955,0.01892491],"genre_scores_gemma":[0.9626397,0.0008548505,0.02149636,0.001100812,0.0002080807,0.001779283,0.01049518,0.00005033325,0.001375376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00804874,"threshold_uncertainty_score":0.04256636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04081411402640369,"score_gpt":0.2679433061593826,"score_spread":0.2271291921329789,"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."}}