{"id":"W4416916263","doi":"10.63530/ijcsitr_2025_06_03_007","title":"Endpoint Security for Healthcare Devices: Protecting Patient Data on Windows and Samsung Assets","year":2025,"lang":"","type":"article","venue":"","topic":"Information and Cyber Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian MPS Society for Mucopolysaccharide and Related Diseases","funders":"","keywords":"Health care; Patient data; Health data; Patient privacy; Point (geometry); Work (physics)","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":[0.003185687,0.0006529354,0.0003280002,0.0008844007,0.0007799809,0.004300868,0.001219262,0.001514113,0.002339252],"category_scores_gemma":[0.004702439,0.0002285991,0.0006548754,0.0004893389,0.0012346,0.005182691,0.002611023,0.001405311,0.0009720223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009147104,"about_ca_system_score_gemma":0.002013619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000892932,"about_ca_topic_score_gemma":0.0006425122,"domain_scores_codex":[0.9977461,0.0008045656,0.0002192828,0.0002317805,0.000767817,0.0002305312],"domain_scores_gemma":[0.9976248,0.0006072861,0.0003364893,0.0008289784,0.0004121174,0.0001904282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006164616,0.0003624576,0.01814633,0.000639444,0.0001232931,0.00117198,0.002211032,0.01235877,0.04029269,0.3902548,0.01273402,0.5210887],"study_design_scores_gemma":[0.0001594885,0.003055549,0.02412944,0.001799462,0.0004472954,0.008795858,0.004083722,0.1996315,0.2084305,0.2093575,0.3397642,0.0003454737],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1362913,0.002980709,0.804665,0.005941876,0.000320286,0.0007668305,0.0002537283,0.005041153,0.04373902],"genre_scores_gemma":[0.7087078,0.002426846,0.2759105,0.00105479,0.000146406,0.0002756181,0.0003720143,0.0002196421,0.01088646],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.004300868,"threshold_uncertainty_score":0.01684773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03934413034995124,"score_gpt":0.3134176924340688,"score_spread":0.2740735620841175,"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."}}