{"id":"W3208476150","doi":"10.1113/jp282456","title":"Tandem cardiovascular system modelling and machine learning for improved heart failure phenotyping","year":2021,"lang":"en","type":"article","venue":"The Journal of Physiology","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ted Rogers Centre for Heart Research; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Heart failure; Tandem; Model system; Training set; Predictive modelling","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002296905,0.001426017,0.00175424,0.001293031,0.0004376939,0.001469614,0.00141421,0.001394922,0.003106871],"category_scores_gemma":[0.006521251,0.0006660774,0.00191716,0.001350292,0.000502197,0.000940244,0.001772812,0.002661596,0.001580336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000702646,"about_ca_system_score_gemma":0.00137632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009147761,"about_ca_topic_score_gemma":0.008304866,"domain_scores_codex":[0.99927,0.0003490952,0.00004611277,0.0001714402,0.00009769683,0.00006564292],"domain_scores_gemma":[0.9975737,0.001631171,0.0001791799,0.0002518683,0.0002659526,0.00009817741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002215936,0.0001581468,0.01102525,0.0002554612,0.0005151766,0.0002174196,0.0001140216,0.8077821,0.002386337,0.006678861,0.00663176,0.1640139],"study_design_scores_gemma":[0.000007007752,0.0000195316,0.0007476729,0.0000183424,0.00001867546,0.00002558323,0.000007586239,0.9902881,0.0002129919,0.007702081,0.0009417699,0.00001055659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01871706,0.003293915,0.9702976,0.00162496,0.0002442754,0.00006688699,0.001327117,0.002864075,0.001564151],"genre_scores_gemma":[0.6489842,0.00366633,0.3339087,0.0009622977,0.0008207229,0.0003604016,0.006401869,0.0005381827,0.004357443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009147761,"threshold_uncertainty_score":0.01818901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01866630967187497,"score_gpt":0.2279203848951299,"score_spread":0.2092540752232549,"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."}}