{"id":"W4413467589","doi":"10.2196/74117","title":"Interpretable Machine Learning Model for Pulmonary Hypertension Risk Prediction: Retrospective Cohort Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Pulmonary Hypertension Research and Treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Medicine; Retrospective cohort study; Cohort; Pulmonary hypertension; Computer science; Artificial intelligence; Machine learning; Internal medicine; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006906039,0.0008105452,0.0008620584,0.001397399,0.0004574581,0.001118262,0.001029131,0.0008552187,0.002673455],"category_scores_gemma":[0.01381592,0.0003504476,0.001494639,0.0009204758,0.0002969339,0.0006331004,0.0006477809,0.001920124,0.0005945202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004611953,"about_ca_system_score_gemma":0.0006746578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00361023,"about_ca_topic_score_gemma":0.001990236,"domain_scores_codex":[0.9985258,0.0006383117,0.000123491,0.0003878153,0.000190231,0.000134446],"domain_scores_gemma":[0.9934887,0.003556867,0.0008606446,0.00112911,0.0006888256,0.0002758748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009757893,0.0003950106,0.9771179,0.00004615621,0.0006111041,0.0005955561,0.0001351998,0.007675558,0.0002770945,0.0003184117,0.001668207,0.01018393],"study_design_scores_gemma":[0.0002227877,0.001951335,0.5848684,0.0001336023,0.00123808,0.002614224,0.0007417763,0.4010705,0.0006887251,0.002649177,0.003725063,0.00009625703],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984985,0.0004911866,0.01124636,0.0002392738,0.00005306363,0.00009496947,0.002468101,0.00004881989,0.0003731412],"genre_scores_gemma":[0.9931593,0.0002406019,0.00301926,0.00004548766,0.00004741702,0.0001148605,0.002985294,0.00001483566,0.0003728425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006906039,"threshold_uncertainty_score":0.03652304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01706778579082968,"score_gpt":0.3089506775980751,"score_spread":0.2918828918072454,"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."}}