{"id":"W4281703424","doi":"10.2196/34724","title":"Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Potassium and Related Disorders","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; Ministry of Trade, Industry and Energy; Ministry of Food and Drug Safety; Korea Medical Device Development Fund","keywords":"Hyperkalemia; Medicine; Emergency department; Kidney disease; Internal medicine; Cardiology; Intensive care medicine; Emergency medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000807477,0.0001795596,0.0003831291,0.0001325522,0.0005226742,0.00003585758,0.00008340226,0.0001293063,0.00003336991],"category_scores_gemma":[0.0003686362,0.000151373,0.0000654449,0.0002430303,0.0001460806,0.0001091338,0.0002217398,0.0006994091,0.000001053152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001206515,"about_ca_system_score_gemma":0.0002453183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005975341,"about_ca_topic_score_gemma":0.000003622828,"domain_scores_codex":[0.9980858,0.00006710371,0.0005914551,0.0001618179,0.0007193111,0.0003744889],"domain_scores_gemma":[0.9992013,0.0002008561,0.0001428749,0.0001330112,0.00006526612,0.0002566864],"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":[0.0008192938,0.001785312,0.2044901,0.001214512,0.0009795961,0.0001193656,0.07381294,0.0002889551,0.0002400759,0.00004051004,0.0002580463,0.7159513],"study_design_scores_gemma":[0.03532235,0.01211034,0.04033193,0.0005688762,0.001455881,0.004470046,0.2852584,0.5232878,0.0005754915,0.0001099551,0.09464256,0.001866371],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880427,0.0003223866,0.009684332,0.0001288352,0.00005360611,0.001360219,0.000001714989,0.00007530379,0.0003309725],"genre_scores_gemma":[0.9832193,0.00002729766,0.01586307,0.0005252896,0.00003794184,0.0002161699,0.00003598808,0.00001946627,0.00005546474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7140849,"threshold_uncertainty_score":0.617281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02568322297405105,"score_gpt":0.2898696293912475,"score_spread":0.2641864064171964,"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."}}