{"id":"W4410194857","doi":"10.1016/j.ins.2025.122273","title":"ECG-STAR: Spatio-temporal attention residual networks for multi-label ECG abnormality classification","year":2025,"lang":"en","type":"article","venue":"Information Sciences","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"National Science and Technology Council","keywords":"Residual; Abnormality; Computer science; Artificial intelligence; Pattern recognition (psychology); Data mining; Medicine; Algorithm","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":[0.001158503,0.001369135,0.0008445623,0.000925391,0.0003706694,0.0006450487,0.001859383,0.001461399,0.003738475],"category_scores_gemma":[0.002662334,0.0004524133,0.0008188589,0.0007654834,0.0002547347,0.0008557661,0.001309432,0.001508526,0.001463538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006924107,"about_ca_system_score_gemma":0.0008577298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0183785,"about_ca_topic_score_gemma":0.03120575,"domain_scores_codex":[0.999656,0.00007648336,0.00001599663,0.000132309,0.00006213222,0.00005722186],"domain_scores_gemma":[0.9993789,0.0002879931,0.00004716312,0.00008833696,0.0001495368,0.00004801835],"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":[0.000845138,0.0004665637,0.003067884,0.0001892877,0.0003526061,0.0002540715,0.00008813747,0.1519535,0.01557069,0.003116339,0.03225243,0.7918434],"study_design_scores_gemma":[0.00002146836,0.00007345452,0.0005368057,0.000009718256,0.00003572524,0.00003830665,0.00001468368,0.9923728,0.002917641,0.00251352,0.001455669,0.00001026077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0590225,0.00240407,0.9123086,0.0008400215,0.0004575635,0.0002178955,0.003071756,0.01835811,0.003319468],"genre_scores_gemma":[0.5802887,0.001064148,0.3935683,0.0009162885,0.0003088078,0.0002532881,0.007966219,0.0008030141,0.01483113],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0183785,"threshold_uncertainty_score":0.03654307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0995800255986423,"score_gpt":0.386605539676485,"score_spread":0.2870255140778427,"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."}}