{"id":"W1977218947","doi":"10.1186/1472-6963-9-s1-a1","title":"A comparison of frequentist and Bayesian approaches to the estimation of long-stay per-diems","year":2009,"lang":"en","type":"article","venue":"BMC Health Services Research","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Health Information","funders":"","keywords":"Frequentist inference; Medicine; Health administration; Bayesian probability; Nursing research; Health informatics; Health economics; Statistics; Case mix index; Emergency medicine; Public health; Bayesian inference; Mathematics; Nursing","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.005096713,0.0001132605,0.0003873837,0.0002745762,0.001362715,0.00002675568,0.0002786803,0.0001508943,0.00006015273],"category_scores_gemma":[0.0001115928,0.00008328188,0.00002834334,0.0006326634,0.00006876446,0.0001310842,0.00008171322,0.0005841292,0.00002445587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001379515,"about_ca_system_score_gemma":0.001056118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003037052,"about_ca_topic_score_gemma":0.007668175,"domain_scores_codex":[0.9954472,0.002014988,0.00102084,0.0002946241,0.0006408839,0.0005815015],"domain_scores_gemma":[0.9978205,0.0006317467,0.0002719469,0.0004720134,0.0005046544,0.0002991413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003927978,0.000745066,0.5773305,0.01582229,0.00002782743,8.246851e-7,0.2108486,0.05541858,0.0001026967,0.01501381,0.0005791775,0.1237177],"study_design_scores_gemma":[0.0004423577,0.0007517802,0.3987269,0.0009425925,0.000005069792,9.900951e-7,0.02294536,0.5752075,0.00006329818,0.0002733417,0.0005359612,0.0001048183],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8703052,0.002205005,0.1035026,0.01815462,0.0001607767,0.004460979,0.00005201494,0.00004570768,0.001113172],"genre_scores_gemma":[0.9648935,0.00009873413,0.03396807,0.000592774,0.00008752654,0.0001035703,0.00007236834,0.00001385109,0.0001696593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5197889,"threshold_uncertainty_score":0.9999374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3548528216601124,"score_gpt":0.5550459471850003,"score_spread":0.2001931255248879,"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."}}