{"id":"W4409804121","doi":"10.55885/jchp.v5i1.418","title":"Law Enforcement Against Medical Personnel as Perpetrators of Fraud in the National Health Insurance Program","year":2025,"lang":"en","type":"article","venue":"Journal of Community Health Provision","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Law enforcement; Business; Enforcement; Actuarial science; Insurance fraud; Environmental health; Medical emergency; Criminology; Law; Medicine; Political science; Psychology","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":[0.006174885,0.000177092,0.0002179052,0.001661283,0.005309424,0.00329888,0.001077619,0.001247019,0.005184024],"category_scores_gemma":[0.02425583,0.0003127315,0.0002169301,0.001202378,0.002205011,0.001596041,0.00362718,0.002073399,0.0003810686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002446114,"about_ca_system_score_gemma":0.007558225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009657995,"about_ca_topic_score_gemma":0.01016608,"domain_scores_codex":[0.9906476,0.004908957,0.0005226181,0.00042235,0.001996425,0.001502124],"domain_scores_gemma":[0.9814162,0.00474081,0.009419195,0.0008063171,0.001356614,0.002260766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001181654,0.001018027,0.6269253,0.0005373169,0.00005588381,0.009078809,0.1500601,0.0001189731,0.0007349579,0.0293371,0.01674843,0.1652669],"study_design_scores_gemma":[0.00004210579,0.0004275396,0.6294812,0.002975272,0.0001148892,0.009534391,0.2644313,0.002061036,0.001487999,0.00396182,0.08541198,0.00007044349],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.952268,0.001435925,0.0006367986,0.008695303,0.0001453906,0.0001390179,0.00003382713,0.00001425352,0.03663136],"genre_scores_gemma":[0.9946597,0.0007535217,0.0002877314,0.001062554,0.00004514925,0.00003355924,0.00001619617,0.000004024055,0.003137601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009657995,"threshold_uncertainty_score":0.03265631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03911564648402913,"score_gpt":0.4015924014227024,"score_spread":0.3624767549386733,"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."}}