{"id":"W7126046435","doi":"10.1109/bibm66473.2025.11356841","title":"Efficient Data Integrity Verification Scheme Based on Multi-Branch Authentication Tree for Electronic Health Record","year":2025,"lang":"","type":"article","venue":"","topic":"Cryptographic Implementations and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Data integrity; Authentication (law); Scheme (mathematics); Tree (set theory); Message authentication code; Enhanced Data Rates for GSM Evolution; Data security; Confidentiality; Data verification; Information privacy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003015491,0.0003552154,0.0003862691,0.0006297224,0.0008942887,0.0004048441,0.002002445,0.0001463238,0.00008109279],"category_scores_gemma":[0.0002411774,0.0003609127,0.0001772088,0.001971324,0.0001100768,0.0002318558,0.0002355854,0.0004971681,0.00002082867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005267487,"about_ca_system_score_gemma":0.002293464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005828398,"about_ca_topic_score_gemma":0.001978167,"domain_scores_codex":[0.9956337,0.0003301177,0.001075676,0.001618906,0.0004312524,0.0009103405],"domain_scores_gemma":[0.9954012,0.0004154773,0.0004154596,0.003230957,0.0003784348,0.0001584525],"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":[0.0001928574,0.003156692,0.0007091665,0.0003294089,0.00008671752,1.149116e-7,0.0008035648,0.00038005,0.0005819374,0.4043102,0.005733239,0.583716],"study_design_scores_gemma":[0.002331782,0.0004938269,0.005667703,0.0001232976,0.00003539858,2.602497e-7,0.0001259444,0.9759555,0.0004089211,0.001512151,0.01305518,0.00029002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01198124,0.0002220027,0.965545,0.01793765,0.001218337,0.002607867,0.000209964,0.0001385586,0.0001394035],"genre_scores_gemma":[0.8785833,0.00009225298,0.1183223,0.001909097,0.00005167953,0.0002117731,0.0006345409,0.00001465654,0.0001804583],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9755754,"threshold_uncertainty_score":0.9998843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07816184379487977,"score_gpt":0.3821210973675394,"score_spread":0.3039592535726596,"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."}}