{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002468814,0.0004499137,0.0008222079,0.0004862966,0.001918059,0.00003838453,0.0007112256,0.0005643126,0.0001643169],"category_scores_gemma":[0.0008344938,0.0002864034,0.0001961281,0.0008263017,0.0002887654,0.00009932606,0.0002539166,0.002023178,0.001796429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007627401,"about_ca_system_score_gemma":0.000995703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001650426,"about_ca_topic_score_gemma":0.002460977,"domain_scores_codex":[0.9934191,0.002135585,0.001247671,0.0007591398,0.0008249918,0.001613578],"domain_scores_gemma":[0.9933401,0.004482855,0.0003924542,0.001260635,0.0001791692,0.0003448337],"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":[0.0005820962,0.00003358811,0.007142974,0.005314476,0.0001185329,0.0001066559,0.001862436,0.00001676076,0.0001272601,0.07401817,0.8340057,0.07667135],"study_design_scores_gemma":[0.0008672657,0.002394609,0.004143176,0.00369437,0.00002455164,0.00001702213,0.00269678,0.00004751206,0.001681154,0.003265381,0.9808648,0.0003033377],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2062385,0.1749291,0.0001354357,0.581531,0.01311846,0.006702917,0.0003239325,0.002498857,0.01452177],"genre_scores_gemma":[0.9905401,0.001971881,0.0001283756,0.004327515,0.0007310104,0.0005341412,0.00003554918,0.0001124902,0.001618865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7843016,"threshold_uncertainty_score":0.9999588,"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."}}