{"id":"W4302276133","doi":"10.19102/icrm.2022.130903","title":"Automated Identification of Ventricular Tachycardia Ablation Targets: Multicenter Validation and Workflow Characterization","year":2022,"lang":"en","type":"article","venue":"Journal of Innovations in Cardiac Rhythm Management","topic":"Cardiac Arrhythmias and Treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University Health Network","funders":"","keywords":"Ventricular tachycardia; False positive paradox; Artificial intelligence; Ablation; Medicine; Gold standard (test); Workflow; Tachycardia; True positive rate; Multicenter study; Automated method; Computer science; Pattern recognition (psychology); Cardiology; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001072621,0.0001178926,0.0003770466,0.0009030208,0.0001026615,0.00002592155,0.00003891062,0.00003550189,0.00002072025],"category_scores_gemma":[0.00005072307,0.0001188534,0.000155506,0.00121827,0.00002289081,0.0002619889,0.00007632034,0.0001554777,0.000001446686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003485488,"about_ca_system_score_gemma":0.00003559466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004141912,"about_ca_topic_score_gemma":8.993938e-8,"domain_scores_codex":[0.9980469,0.0001366155,0.001001856,0.0001608355,0.0005275053,0.000126263],"domain_scores_gemma":[0.9985361,0.00002900089,0.0008332212,0.0002031087,0.0003645298,0.00003411324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001324166,0.002324746,0.7636235,0.0009779305,0.005279417,0.001944266,0.002172475,0.03484629,0.01984526,0.006514466,0.002739897,0.1584076],"study_design_scores_gemma":[0.002501814,0.0001384828,0.9838955,0.0001215987,0.0004760032,0.0002324171,0.0004119926,0.00443757,0.00238526,0.0001036812,0.005164544,0.0001311287],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918457,0.0003461347,0.005289935,0.000511509,0.000908179,0.0007864824,0.00006513072,0.00002741815,0.0002195638],"genre_scores_gemma":[0.9970722,0.0002405576,0.0019202,0.00005180467,0.0001054655,0.00006869693,0.0004516742,0.00001680608,0.00007256711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.220272,"threshold_uncertainty_score":0.4846702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008029428513913908,"score_gpt":0.2612677648774569,"score_spread":0.253238336363543,"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."}}