{"id":"W1964310537","doi":"10.1016/j.jelectrocard.2008.02.010","title":"On designing and testing transformations for derivation of standard 12-lead/18-lead electrocardiograms and vectorcardiograms from reduced sets of predictor leads","year":2008,"lang":"en","type":"article","venue":"Journal of Electrocardiology","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Lead (geology); Statistics; Mathematics; Standard deviation; Cardiology; Medicine; QRS complex; Vectorcardiography; Internal medicine; Electrocardiography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005728038,0.0001861395,0.001145287,0.0003851352,0.000120441,0.000007636226,0.00007188223,0.000166009,0.000001149746],"category_scores_gemma":[0.0007033955,0.0001573224,0.0003686511,0.0003191449,0.0001550075,0.0001066349,0.000008367724,0.0003281056,9.471223e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006856875,"about_ca_system_score_gemma":0.0002100687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001988529,"about_ca_topic_score_gemma":0.000001522334,"domain_scores_codex":[0.9983105,0.0001382483,0.0007690722,0.0001890057,0.0003003578,0.0002928203],"domain_scores_gemma":[0.9979795,0.0007317238,0.0005167596,0.00014589,0.0005190658,0.0001070748],"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":[0.002156161,0.00006452368,0.1458036,0.0001355065,0.002277564,0.00002283716,0.0005395142,0.0003492402,0.8280236,0.00003043028,0.000275458,0.02032162],"study_design_scores_gemma":[0.01054642,0.02953059,0.2059931,0.001137477,0.004594052,0.003326369,0.0005275818,0.003403374,0.7375914,0.002591413,0.0002430629,0.0005151441],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9295186,0.001964873,0.06793171,0.0001049676,0.00008357361,0.0002151254,0.0000142666,0.00001712543,0.0001497372],"genre_scores_gemma":[0.9825538,0.000994754,0.01605334,0.00002433961,0.0003173777,0.000007893105,0.00001889575,0.00002338203,0.000006215634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0904322,"threshold_uncertainty_score":0.641542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03725942680280938,"score_gpt":0.2839903115009191,"score_spread":0.2467308846981097,"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."}}