{"id":"W1819256023","doi":"","title":"Determining the technical efficiency of hospitals using Data Envelopment Analysis: a national-wide study on governmental hospitals in Iran.","year":2010,"lang":"en","type":"article","venue":"Lund University Publications (Lund University)","topic":"Healthcare Systems and Reforms","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Health Economics","funders":"","keywords":"Data envelopment analysis; Business; Operations research; Operations management; Statistics; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0008556735,0.0001410775,0.0003336338,0.001363117,0.0004301024,0.00005101645,0.001187661,0.0001251957,0.00005037842],"category_scores_gemma":[0.000211784,0.0001392158,0.0001128785,0.00268785,0.0001349879,0.0006447419,0.0004191959,0.0002928438,0.00001351909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000870986,"about_ca_system_score_gemma":0.0001589532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001648208,"about_ca_topic_score_gemma":0.001972875,"domain_scores_codex":[0.9986015,0.00005063153,0.0003932714,0.0005372008,0.0001738221,0.0002435821],"domain_scores_gemma":[0.9984408,0.00009805273,0.0004397847,0.0008004108,0.0001024876,0.0001184346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009066138,0.0005402246,0.7134806,0.000006521029,0.0001679348,0.000004090627,0.0004798449,0.00006642324,0.00001182513,0.2850811,0.00005591701,0.00009644835],"study_design_scores_gemma":[0.0007716891,0.00008795952,0.8683468,0.000009316166,0.00004573384,8.606798e-7,0.005379537,0.001104832,0.000003384245,0.00008463066,0.1239592,0.0002061173],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8054415,0.000008786559,0.0008534706,0.0005781896,0.0001036469,0.000488462,0.0003364605,0.00002485663,0.1921646],"genre_scores_gemma":[0.992057,0.000014373,0.0003476425,0.00003525641,0.00001729268,5.225011e-7,0.00006308046,0.000008135821,0.00745674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2849964,"threshold_uncertainty_score":0.5677058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06431260828003638,"score_gpt":0.2652566192733256,"score_spread":0.2009440109932892,"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."}}