{"id":"W4409640280","doi":"10.2196/71777","title":"Advancing Emergency Care With Digital Twins","year":2025,"lang":"en","type":"editorial","venue":"JMIR Aging","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Analytics; Wearable computer; Data science; Computer science; Predictive analytics; Paradigm shift; Wearable technology; Big data; Corporate governance; Knowledge management; Business; Data mining","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":[0.00007358222,0.0002426767,0.0003494327,0.0002044093,0.000152246,0.00004924523,0.0001137818,0.0003421084,0.0001199666],"category_scores_gemma":[0.0003453244,0.0002095653,0.00009827341,0.0002993349,0.00002723269,0.000160702,0.00004056861,0.000794258,0.00005835175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004015176,"about_ca_system_score_gemma":0.001563716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003494527,"about_ca_topic_score_gemma":0.0001734565,"domain_scores_codex":[0.9982967,0.00001391623,0.0004071708,0.0004211965,0.0004780192,0.0003829539],"domain_scores_gemma":[0.9984902,0.0001739089,0.000136799,0.0003744227,0.0006786898,0.0001459982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005512239,0.00003747165,0.005611829,0.001857354,0.00003260585,0.00002443554,0.003255987,0.00000531921,0.000004540733,0.000004467021,0.9332749,0.05583601],"study_design_scores_gemma":[0.00004349306,0.0001779921,0.00007238677,0.002363164,0.00008072364,0.000002657346,0.006212981,0.00001196218,0.0001510434,0.00004989887,0.990615,0.0002186689],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.004644745,0.001825031,0.0003662125,0.000584589,0.9742565,0.0008530987,0.00006781956,0.0001873031,0.01721471],"genre_scores_gemma":[0.04131497,0.000338967,0.0001780533,0.00007788344,0.9458028,0.000171322,0.001687107,0.00006284922,0.01036607],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.05734017,"threshold_uncertainty_score":0.8545826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02896911213518309,"score_gpt":0.4076225008403718,"score_spread":0.3786533887051887,"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."}}