{"id":"W3008584713","doi":"10.5430/jha.v9n1p43","title":"Patient transfers during hospitalization: An examination of intra facility patient locations using network analysis","year":2020,"lang":"en","type":"article","venue":"Journal of Hospital Administration","topic":"Patient Satisfaction in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Patient care; Medicine; Service (business); Medical emergency; Emergency medicine; Healthcare service; Patient data; Health care; Computer science; Nursing; Database; Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004047265,0.0001981384,0.0004915718,0.0002272583,0.0004794725,0.00001757762,0.0001306776,0.0001928135,0.0001880608],"category_scores_gemma":[0.0002930768,0.0001965606,0.0002059061,0.001124872,0.00006588534,0.0008836461,0.00002391102,0.0004578063,0.000004490469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003525054,"about_ca_system_score_gemma":0.0005560247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004099978,"about_ca_topic_score_gemma":0.00005718202,"domain_scores_codex":[0.9958018,0.000753129,0.00216535,0.0002642955,0.0007183073,0.0002971301],"domain_scores_gemma":[0.9959806,0.0001266963,0.001955653,0.0002243571,0.001367323,0.0003454355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002974748,0.0003168308,0.9231353,0.0004204763,0.0003414016,0.00001295981,0.03009175,0.04059829,0.0005168712,0.0003490141,0.00005029373,0.003869308],"study_design_scores_gemma":[0.0006737949,0.01142841,0.9660346,0.0001848551,0.000429925,0.00000394017,0.01262092,0.006885016,0.001358287,0.00008996105,0.00002505313,0.0002652383],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768561,0.00006396618,0.02078007,0.0006607571,0.0006654072,0.0007367805,0.0001343547,0.00003043694,0.00007210028],"genre_scores_gemma":[0.9978101,0.00002291088,0.001623595,0.000114629,0.0002577112,0.00001843968,0.0001353761,0.00001524914,0.000001993365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04289927,"threshold_uncertainty_score":0.8015508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04986140646797912,"score_gpt":0.3610107458013536,"score_spread":0.3111493393333744,"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."}}