{"id":"W4367841486","doi":"10.32920/22734314","title":"Lightweight and Interpretable Left Ventricular Ejection Fraction Estimation using Mobile U-Net","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Ejection fraction; Fraction (chemistry); Artificial intelligence; Computer science; Ventricle; Pipeline (software); Frame (networking); Net (polyhedron); Cardiology; Medicine; Mathematics; Telecommunications; Heart failure; Geometry","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":[0.0002231454,0.00101553,0.0005420519,0.0006771691,0.000203826,0.0004750335,0.001016996,0.000670701,0.003528617],"category_scores_gemma":[0.000771681,0.0002844854,0.0003717352,0.0003243616,0.0001636065,0.0005960421,0.0007460315,0.0005315284,0.001633637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003804308,"about_ca_system_score_gemma":0.0003680261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005961413,"about_ca_topic_score_gemma":0.01189415,"domain_scores_codex":[0.999882,0.00001323658,0.000006118788,0.00004721851,0.00002840408,0.00002307019],"domain_scores_gemma":[0.9998571,0.00004270757,0.00002259197,0.00002606224,0.00003526691,0.00001625729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007861473,0.0002285589,0.006282039,0.0001329966,0.0001124286,0.0006147343,0.00006081849,0.08389307,0.02351052,0.001362882,0.020613,0.8624027],"study_design_scores_gemma":[0.00002962626,0.00006889496,0.002380529,0.00002636551,0.00002095105,0.0002136817,0.00003077496,0.9807613,0.01072538,0.00251657,0.003210149,0.00001571576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08560541,0.001278556,0.8832939,0.0004494028,0.0002232074,0.000167043,0.004158272,0.02121387,0.003610278],"genre_scores_gemma":[0.625346,0.000731389,0.3524139,0.0004464198,0.0001607462,0.0002745896,0.0102882,0.0004173404,0.009921486],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005961413,"threshold_uncertainty_score":0.01185346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.020089710414661,"score_gpt":0.2838905236120534,"score_spread":0.2638008131973923,"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."}}