{"id":"W4380793675","doi":"10.1161/circ.146.suppl_1.10580","title":"Abstract 10580: Machine Learning-Assisted Echocardiographic Identification of Children at High Risk for Treatment-Related Cardiomyopathy: A Proof-of-Concept Study","year":2022,"lang":"en","type":"article","venue":"Circulation","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Medicine; Parasternal line; Heart failure; Artificial intelligence; Proof of concept; Machine learning; Binary classification; Identification (biology); Internal medicine; Cardiology; Computer science; Support vector machine","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":[0.003360628,0.0009402296,0.0007380961,0.0002733229,0.0001819215,0.0007136699,0.001190811,0.001267332,0.003205249],"category_scores_gemma":[0.002775023,0.0002343323,0.0006713389,0.0001297356,0.000556175,0.0007323361,0.0006950939,0.001604089,0.001187744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003819704,"about_ca_system_score_gemma":0.001232706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001164338,"about_ca_topic_score_gemma":0.0007522323,"domain_scores_codex":[0.9993201,0.0002052706,0.0000291926,0.0001341347,0.0002193433,0.00009196285],"domain_scores_gemma":[0.9985733,0.0003837641,0.000156641,0.0001486386,0.0004398304,0.0002978225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01785228,0.02483975,0.04725935,0.004712944,0.002963994,0.002573434,0.0004183134,0.076717,0.4451113,0.004747027,0.07860619,0.2941984],"study_design_scores_gemma":[0.007942687,0.1221309,0.05644865,0.0006605915,0.001525196,0.003368804,0.0004455652,0.3063546,0.4257921,0.00375091,0.07127704,0.0003030546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8985186,0.003456316,0.076741,0.002445301,0.001183962,0.002135021,0.007485597,0.002176917,0.00585731],"genre_scores_gemma":[0.9032636,0.001858454,0.07207049,0.002636246,0.000333961,0.003419503,0.01092426,0.0002571473,0.005236432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003360628,"threshold_uncertainty_score":0.01777291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01358848213803098,"score_gpt":0.2436630591398207,"score_spread":0.2300745770017897,"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."}}