{"id":"W4402731153","doi":"10.61859/hacettepesid.1406500","title":"THE EFFECT OF HEALTHCARE TECHNOLOGY ON HEALTH EXPENDITURES","year":2024,"lang":"en","type":"article","venue":"Hacettepe sağlık idaresi dergisi","topic":"Global Health Care Issues","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Magnetic resonance imaging; Computed tomography; Czech; Health care; Medicine; Medical imaging; Tomography; Scope (computer science); Nuclear medicine; Radiology; Economic growth; Economics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009278149,0.0002699145,0.0003148,0.002269808,0.0002117642,0.001226322,0.0002264583,0.0004030078,0.004008416],"category_scores_gemma":[0.00664526,0.0001427497,0.001252828,0.002957981,0.0003070631,0.000745906,0.000806356,0.0007994213,0.000353023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001335369,"about_ca_system_score_gemma":0.0008070744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0125475,"about_ca_topic_score_gemma":0.01200732,"domain_scores_codex":[0.9972247,0.00110058,0.0002119395,0.0002659032,0.0006280231,0.0005688404],"domain_scores_gemma":[0.9932262,0.003135197,0.002622012,0.0002214403,0.0004222883,0.0003728274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001687969,0.00009542556,0.9810717,0.00008947054,0.00053257,0.0002569605,0.00007231753,0.003876521,0.0002896839,0.00212116,0.0006576368,0.01076784],"study_design_scores_gemma":[0.000004386619,0.00006724206,0.9963803,0.00003171595,0.0001100114,0.0001011822,0.0001020479,0.0009709034,0.000188562,0.0002186928,0.001819205,0.000005821488],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9702562,0.003120762,0.001063127,0.001283072,0.00005507198,0.00006680866,0.005352887,0.00002185595,0.0187803],"genre_scores_gemma":[0.9946537,0.001134314,0.0002809843,0.00009792403,0.00005125822,0.00002061869,0.002001318,0.0000046296,0.001755387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0125475,"threshold_uncertainty_score":0.02494889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01950803775431151,"score_gpt":0.4411004291688326,"score_spread":0.4215923914145211,"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."}}