{"id":"W2893024594","doi":"10.1016/j.cjca.2018.07.181","title":"REDUCING DELAY TO TREATMENT OF ST-ELEVATION MYOCARDIAL INFARCTION WITH THE USE OF SOFTWARE ELECTROCARDIOGRAPHIC INTERPRETATION AND ELECTRONIC TRANSMISSION (SCINET)","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Research Manitoba","funders":"","keywords":"Medicine; Myocardial infarction; Emergency department; Cardiac catheterization; Population; Internal medicine; Cardiology; Gold standard (test); ST elevation; Electrocardiography; Medical emergency; Emergency medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0002139929,0.00009210176,0.0004021624,0.000476647,0.0000687739,0.000007823953,0.00003825764,0.00006522093,0.000002026977],"category_scores_gemma":[0.00006610082,0.00005740479,0.000162627,0.0003459287,0.000129322,0.00006283306,0.000001334248,0.0001056234,1.443139e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001488425,"about_ca_system_score_gemma":0.0005200987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001049805,"about_ca_topic_score_gemma":0.0006404661,"domain_scores_codex":[0.9992235,0.0001185499,0.0002568771,0.000104483,0.0001098086,0.0001868167],"domain_scores_gemma":[0.999024,0.00007197256,0.0001570877,0.0001233893,0.0004431746,0.0001803841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002482759,0.00001367012,0.4396527,0.00005367635,0.004683251,0.00003276179,0.00408112,0.01732091,0.03855975,0.00003132083,0.001123794,0.4919643],"study_design_scores_gemma":[0.005316982,0.0503464,0.7579995,0.0009462265,0.007402983,0.002956102,0.0009816638,0.002085335,0.01863126,0.0001691488,0.1526835,0.0004808329],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9372187,0.0006361469,0.06127486,0.0006383568,0.00007516188,0.0001241212,0.000003531335,0.000002877719,0.00002620686],"genre_scores_gemma":[0.9984837,0.0002756729,0.0006997138,0.0000394379,0.0004694596,0.000002981792,0.000004975365,0.00000969259,0.00001436896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4914834,"threshold_uncertainty_score":0.2340899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01400949506033361,"score_gpt":0.2424275030107705,"score_spread":0.2284180079504369,"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."}}