{"id":"W3028192330","doi":"10.1016/j.jval.2020.04.1239","title":"PRO8 MEDICATIONS FOR ACROMEGALY: COST-UTILITY AND VALUE OF INFORMATION ANALYSIS","year":2020,"lang":"en","type":"article","venue":"Value in Health","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Public Health","funders":"","keywords":"Pegvisomant; Medicine; Lanreotide; Acromegaly; Cost–utility analysis; Per capita; Actuarial science; Cohort; Cost effectiveness; Internal medicine; Economics; Environmental health; Risk analysis (engineering)","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":[0.006482091,0.001021902,0.001090611,0.003092684,0.0002285705,0.002263263,0.0007874381,0.0009952541,0.007024755],"category_scores_gemma":[0.03091749,0.0002879224,0.001432229,0.003358492,0.0006158001,0.002342304,0.0009531326,0.001555508,0.0003315494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003415036,"about_ca_system_score_gemma":0.001693338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00562976,"about_ca_topic_score_gemma":0.003808047,"domain_scores_codex":[0.9946263,0.004205989,0.0001397239,0.0001619248,0.0007018197,0.0001642127],"domain_scores_gemma":[0.9810274,0.01682331,0.0007721544,0.0004894618,0.0006961247,0.0001916027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006991181,0.0007069501,0.05425368,0.001798333,0.002605119,0.0005297847,0.0002136058,0.329757,0.0009206783,0.1264172,0.02150024,0.4543062],"study_design_scores_gemma":[0.0004868203,0.001249343,0.02912718,0.0008400262,0.001796744,0.00101485,0.0003146606,0.8106682,0.001186978,0.1383247,0.01485534,0.0001350479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5892354,0.08665327,0.2303901,0.01811926,0.0004557943,0.001423744,0.01465951,0.0005693882,0.05849349],"genre_scores_gemma":[0.9721084,0.004937324,0.01808238,0.0001847783,0.0001481608,0.0001509593,0.001374183,0.00004981068,0.002963992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007024755,"threshold_uncertainty_score":0.03428102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3939642987130497,"score_gpt":0.4385871465986704,"score_spread":0.04462284788562071,"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."}}