{"id":"W4386453277","doi":"10.1371/journal.pcbi.1010835","title":"iCVS—Inferring Cardio-Vascular hidden States from physiological signals available at the bedside","year":2023,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Hemodynamic Monitoring and Therapy","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Council for Higher Education; Israel Science Foundation","keywords":"Inference; Computer science; Blood pressure; Artificial intelligence; Intensive care medicine; Medicine; Machine learning; Internal medicine","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":[0.001367715,0.001107851,0.0008266022,0.0008092353,0.0003342311,0.001218729,0.001428074,0.001245944,0.001464261],"category_scores_gemma":[0.007322796,0.0006234964,0.00124948,0.0005280069,0.0007903753,0.00118659,0.001935984,0.002231239,0.0002794413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006921372,"about_ca_system_score_gemma":0.001612985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00815449,"about_ca_topic_score_gemma":0.005533507,"domain_scores_codex":[0.9993473,0.0001902928,0.00004176025,0.0002130672,0.0001212634,0.00008621091],"domain_scores_gemma":[0.9965191,0.002685742,0.0002332511,0.0002623683,0.0001849551,0.0001146299],"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":[0.0002445238,0.0001045605,0.01043561,0.0001690877,0.0002350827,0.0003160227,0.0002139071,0.8908494,0.0032032,0.02542114,0.001964812,0.0668426],"study_design_scores_gemma":[0.000007035881,0.00002166301,0.0005616461,0.00001444337,0.00001527138,0.00002762499,0.00001247495,0.9848985,0.0004761392,0.01359091,0.0003636991,0.00001051112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03877568,0.0004043962,0.9579667,0.0005241428,0.00007413896,0.00004562005,0.0005666189,0.0007941856,0.0008484561],"genre_scores_gemma":[0.9088805,0.0006707562,0.08649512,0.0003100754,0.0001669192,0.0001461426,0.001642867,0.00009023029,0.001597385],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00815449,"threshold_uncertainty_score":0.01621401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05148779925650008,"score_gpt":0.2961905630195033,"score_spread":0.2447027637630032,"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."}}