{"id":"W2845366390","doi":"10.1016/j.knosys.2018.07.005","title":"Assessment for hierarchical medical policy proposals using hesitant fuzzy linguistic analytic network process","year":2018,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Consistency (knowledge bases); Construct (python library); Computer science; Analytic network process; Relation (database); Fuzzy logic; Process (computing); Multiplicative function; Control (management); Operations research; Analytic hierarchy process; Order (exchange); Data mining; Management science; Artificial intelligence; Mathematics; Engineering; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01414956,0.0009778528,0.001262786,0.004598432,0.001770057,0.004685541,0.001813309,0.002641476,0.006486511],"category_scores_gemma":[0.04333762,0.000611082,0.001193474,0.002479743,0.001587306,0.005326814,0.002592258,0.001832768,0.0003648683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004797707,"about_ca_system_score_gemma":0.004685372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004781987,"about_ca_topic_score_gemma":0.004582071,"domain_scores_codex":[0.9934201,0.003574193,0.0003062704,0.0006240378,0.001708627,0.0003667535],"domain_scores_gemma":[0.9754165,0.01926183,0.001571474,0.0004648314,0.002606642,0.0006787106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003111475,0.000179733,0.002621268,0.0002966406,0.0001684258,0.0002745796,0.0007702754,0.8261455,0.0009548186,0.1206455,0.001183654,0.04644845],"study_design_scores_gemma":[0.00001969602,0.00006202153,0.0003142575,0.00003168408,0.00003038683,0.00001561611,0.0001540675,0.9572037,0.0003893477,0.04138164,0.0003771198,0.00002041834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1352123,0.0002342648,0.8540047,0.001352379,0.00009555024,0.000566665,0.0002072289,0.0001764438,0.008150456],"genre_scores_gemma":[0.8444197,0.0001623253,0.1528626,0.00009259924,0.00004754029,0.0004391342,0.0001095216,0.00002082488,0.00184577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01414956,"threshold_uncertainty_score":0.07483095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.163290357797668,"score_gpt":0.5076158952248309,"score_spread":0.3443255374271629,"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."}}