{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001770225,0.000250213,0.0002103244,0.005032631,0.0003544215,0.001084505,0.0003732834,0.0002335955,0.001271029],"category_scores_gemma":[0.01036989,0.000115642,0.0002995829,0.005677771,0.000236382,0.001278938,0.001126956,0.0002874432,0.0002019093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008183019,"about_ca_system_score_gemma":0.0007628229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01138278,"about_ca_topic_score_gemma":0.02106148,"domain_scores_codex":[0.9987621,0.000415531,0.0002726343,0.0001569386,0.0002609321,0.0001318089],"domain_scores_gemma":[0.9925159,0.002744735,0.003378227,0.0002912804,0.0007514918,0.0003183361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006236009,0.00002493881,0.9856611,0.0000525072,0.00004694876,0.0001115554,0.0007880562,0.0006954724,0.0002627514,0.0001189156,0.0004483316,0.01172708],"study_design_scores_gemma":[0.000002321731,0.00008524239,0.9874007,0.00005440238,0.00003035961,0.0002651812,0.004369488,0.005940252,0.0002264266,0.0001739069,0.001436629,0.00001509923],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937303,0.0001772609,0.001579647,0.0002039624,0.000009363704,0.00005226013,0.002411086,0.0000413146,0.001794859],"genre_scores_gemma":[0.9970041,0.0001641901,0.0012863,0.00002387125,0.000007262345,0.00003891157,0.001275709,0.000008043315,0.0001917151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01138278,"threshold_uncertainty_score":0.02263308,"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."}}