{"id":"W6977175244","doi":"10.6084/m9.figshare.27199853.v1","title":"Additional file 3 of Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cardiomyopathy; Cancer; Heart disease; MEDLINE","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008640779,0.001333895,0.001200491,0.001923753,0.0004481663,0.001351761,0.001607066,0.001306092,0.8216078],"category_scores_gemma":[0.01795659,0.0006311331,0.001125555,0.001235888,0.0002055132,0.001016679,0.001115999,0.000685755,0.1791005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005517803,"about_ca_system_score_gemma":0.0008142634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004479934,"about_ca_topic_score_gemma":0.008440681,"domain_scores_codex":[0.9996703,0.00005908091,0.00005652711,0.0001099147,0.00006177112,0.00004249076],"domain_scores_gemma":[0.9904898,0.007693217,0.0003288811,0.0004339235,0.0007887036,0.0002654434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005538278,0.0001608947,0.007893048,0.002330613,0.0001072939,0.0002388487,0.00007230572,0.001387966,0.0003119483,0.0007325756,0.9564987,0.02971206],"study_design_scores_gemma":[0.007128921,0.0006075205,0.06459972,0.00429989,0.0005692506,0.00247055,0.0004730948,0.02227553,0.005458982,0.02753964,0.8642107,0.0003662689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007408794,0.00004213386,0.001917712,0.0001509984,0.00005333853,0.0001157297,0.99306,0.00236522,0.001553974],"genre_scores_gemma":[0.02090625,0.0001797998,0.01649415,0.0007678256,0.000234985,0.00170739,0.9451576,0.002766378,0.01178568],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8216078,"threshold_uncertainty_score":0.2544547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01390195346722314,"score_gpt":0.2415469359859484,"score_spread":0.2276449825187253,"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."}}