{"id":"W2166347772","doi":"10.1016/j.annemergmed.2004.11.023","title":"Developing an Efficient Model to Select Emergency Department Patient Satisfaction Improvement Strategies","year":2005,"lang":"en","type":"article","venue":"Annals of Emergency Medicine","topic":"Patient Satisfaction in Healthcare","field":"Health Professions","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Patient satisfaction; Emergency department; Courtesy; Logistic regression; Ordered logit; Ordinal regression; Quality (philosophy); Family medicine; Nursing; Statistics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008821787,0.0003626091,0.0005124456,0.0003684305,0.0006341087,0.000001389148,0.0001827268,0.0001740448,0.003923493],"category_scores_gemma":[0.0003053911,0.0003125275,0.00009663117,0.0006496955,0.0000302527,0.0002868947,0.00009824497,0.0003899654,0.000129794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003055499,"about_ca_system_score_gemma":0.000528291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002032226,"about_ca_topic_score_gemma":0.00342914,"domain_scores_codex":[0.9947733,0.0003374945,0.002451709,0.0005830376,0.0009426129,0.0009118565],"domain_scores_gemma":[0.9967139,0.00007067174,0.0008120186,0.0005765877,0.00132081,0.0005060488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007744622,0.0005235972,0.3656656,0.002257565,0.0003559835,0.000002796689,0.06416974,0.222819,0.01882304,0.02710819,0.1630617,0.1344382],"study_design_scores_gemma":[0.002174798,0.008805471,0.859706,0.001721874,0.0001616913,0.000001634073,0.03044278,0.05247774,0.006869873,0.008193308,0.02751851,0.001926339],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.970903,0.0005164132,0.01061816,0.00925359,0.004080772,0.002075467,0.00006232807,0.0001387936,0.002351466],"genre_scores_gemma":[0.9941668,0.001366303,0.001843618,0.001083717,0.0005855623,0.0005688462,0.00006423852,0.00004696981,0.000273953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4940403,"threshold_uncertainty_score":0.9999327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2778159120384509,"score_gpt":0.5162044484640311,"score_spread":0.2383885364255803,"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."}}