{"id":"W2797124171","doi":"10.1007/s11538-018-0425-3","title":"HPV Screening and Vaccination Strategies in an Unscreened Population: A Mathematical Modeling Study","year":2018,"lang":"en","type":"article","venue":"Bulletin of Mathematical Biology","topic":"Cervical Cancer and HPV Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; Institut National de Santé Publique du Québec; Université de Montréal; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Vaccination; Cervical cancer; Medicine; HPV infection; Cancer prevention; Population; Human papillomavirus; Cancer; Gynecology; Demography; Immunology; Environmental health; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002256887,0.0009214876,0.00178685,0.001118726,0.000888093,0.002409226,0.002248097,0.003330397,0.007241109],"category_scores_gemma":[0.01010911,0.0008376185,0.002246811,0.0008235423,0.001455129,0.003021624,0.001585946,0.002852656,0.0006452285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002322986,"about_ca_system_score_gemma":0.002259197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02363213,"about_ca_topic_score_gemma":0.01287415,"domain_scores_codex":[0.9992618,0.0003549833,0.00001996492,0.00010558,0.00004854284,0.0002090281],"domain_scores_gemma":[0.9932424,0.005227826,0.0007359013,0.0001414386,0.0002988593,0.0003535054],"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":[0.0001904031,0.0006020682,0.01502285,0.0001349928,0.0002551868,0.0008942867,0.0005778151,0.8553102,0.0008373693,0.1168381,0.003454363,0.005882456],"study_design_scores_gemma":[0.00004201866,0.00009364545,0.001401543,0.00001729851,0.00009986442,0.0001400794,0.0002229396,0.9843346,0.00005031901,0.01312111,0.0004566669,0.00001988828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8414586,0.001865868,0.1206259,0.01249189,0.000160844,0.0001748406,0.0007816706,0.0001111506,0.02232938],"genre_scores_gemma":[0.9685321,0.001087181,0.006375909,0.0006777244,0.0001440894,0.0001452503,0.0002035839,0.00004391379,0.02279027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02363213,"threshold_uncertainty_score":0.04698914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07458411448827322,"score_gpt":0.3937862701205163,"score_spread":0.319202155632243,"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."}}