{"id":"W4361800675","doi":"10.33616/lam.33.0087","title":"A méhnyakrák epidemiológiai mutatói 2000–2019 között Magyarországon megfigyelhető változásának elemzése nemzetközi összevetéssel","year":2023,"lang":"hu","type":"article","venue":"Lege Artis Medicinae","topic":"Iron Metabolism and Disorders","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Health Economics","funders":"","keywords":"Mathematics; Animal science; Physics; Demography; Sociology; Biology","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":[0.004123912,0.0004226293,0.0005708647,0.002403049,0.0007133578,0.00268033,0.0007392886,0.0007976218,0.02339211],"category_scores_gemma":[0.0107844,0.0003233317,0.001071512,0.003697054,0.0005995181,0.001594915,0.002534946,0.001487659,0.004242473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003061483,"about_ca_system_score_gemma":0.005084372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05093196,"about_ca_topic_score_gemma":0.05679268,"domain_scores_codex":[0.9972984,0.00076474,0.0003504268,0.0004966248,0.0007514851,0.0003382183],"domain_scores_gemma":[0.9957793,0.001151275,0.001202861,0.0004118228,0.001132744,0.0003221295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001704554,0.0003416348,0.4904655,0.00453856,0.001362162,0.0005080152,0.005643982,0.002194563,0.002452572,0.03129572,0.2027719,0.2567208],"study_design_scores_gemma":[0.000135473,0.0002229842,0.5775076,0.001763322,0.0005766732,0.0004437712,0.003704302,0.0008900953,0.002331685,0.006429565,0.4059048,0.00008965944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3879181,0.02815083,0.01808313,0.04983874,0.003065502,0.001398601,0.3110082,0.001219129,0.1993178],"genre_scores_gemma":[0.7334067,0.01637923,0.01505318,0.007718885,0.0009642181,0.002315843,0.1091313,0.0005265516,0.114504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05093196,"threshold_uncertainty_score":0.101271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02120974512641063,"score_gpt":0.3016077524527269,"score_spread":0.2803980073263163,"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."}}