{"id":"W2115740690","doi":"10.6000/1929-6029.2015.04.01.9","title":"Some Useful Properties of Log-Logistic Random Variables for Health Care Simulations","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random variable; Mathematics; Sum of normally distributed random variables; Statistics; Logarithm; Logistic regression; Variable (mathematics); Normal distribution; Multivariate random variable; Mathematical analysis","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":[0.009142387,0.001332553,0.0009770539,0.002380529,0.0007769209,0.001799474,0.001768939,0.001548944,0.007992598],"category_scores_gemma":[0.06626511,0.0005270266,0.001736234,0.003550601,0.001488821,0.003886976,0.001404491,0.003887548,0.001771117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001339209,"about_ca_system_score_gemma":0.001549403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002980462,"about_ca_topic_score_gemma":0.002001046,"domain_scores_codex":[0.9964491,0.00228388,0.0002314106,0.0002761802,0.0006032669,0.0001560851],"domain_scores_gemma":[0.9651101,0.02847121,0.001986031,0.001502119,0.002524881,0.0004056789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005124163,0.00007472342,0.003893478,0.000269194,0.00006164755,0.0003827263,0.0002541554,0.2848316,0.0008778766,0.6515368,0.009677724,0.04808882],"study_design_scores_gemma":[0.00002469774,0.00005824938,0.0009018043,0.0001529474,0.00002297538,0.0002584055,0.0000693629,0.5256852,0.0006471179,0.4592619,0.01286496,0.00005237345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003898732,0.00132912,0.9868348,0.001104465,0.0001314031,0.00009582643,0.0004691688,0.000253258,0.005883304],"genre_scores_gemma":[0.3383914,0.007503044,0.6354309,0.001734795,0.001551307,0.001653668,0.0022686,0.0008446658,0.01062159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009142387,"threshold_uncertainty_score":0.0483501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3867722813776298,"score_gpt":0.5983361301163364,"score_spread":0.2115638487387065,"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."}}