{"id":"W4396855522","doi":"10.1016/j.mex.2024.102752","title":"Assessing the impact of arsenic, lead, mercury, and cadmium exposure on glycemic and lipid profile markers: A systematic review and meta-analysis protocol","year":2024,"lang":"en","type":"review","venue":"MethodsX","topic":"Heavy Metal Exposure and Toxicity","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Cadmium; Arsenic; Meta-analysis; Mercury (programming language); Lipid profile; Glycemic; Chemistry; Medicine; Biochemistry; Cholesterol; Internal medicine; Diabetes mellitus; Computer science; Endocrinology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006746552,0.0007053834,0.006579224,0.0001550057,0.0001247957,0.0002012763,0.0003092721,0.0002481666,0.0004182229],"category_scores_gemma":[0.000677947,0.0003092745,0.001914782,0.001079165,0.0003581167,0.0002154254,0.0003609398,0.0005984568,0.00002903589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001099001,"about_ca_system_score_gemma":0.0000744325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002188,"about_ca_topic_score_gemma":0.00001202951,"domain_scores_codex":[0.9923998,0.004577241,0.001384508,0.0008515053,0.0004638034,0.0003231915],"domain_scores_gemma":[0.9968944,0.001115482,0.0009127394,0.000891212,0.00001713927,0.0001690399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.00001170969,0.00005933758,0.00001026302,0.9091058,0.04912419,0.00001398627,0.0001336882,0.000001127498,0.00001420267,0.000008538176,0.0002407496,0.04127643],"study_design_scores_gemma":[0.00012319,0.0004472603,0.00003317917,0.02659342,0.9528561,0.0001868903,0.00003297408,0.00007655036,0.000008392937,0.00007266965,0.01898267,0.0005866981],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001133151,0.9399346,0.000174468,0.00005758668,0.00001707124,0.05916891,0.00007461404,0.00002140212,0.0005400752],"genre_scores_gemma":[0.00004263709,0.9394091,0.006274568,0.000111036,0.00003024683,0.05332101,0.00001269543,0.00006809788,0.0007305493],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9037319,"threshold_uncertainty_score":0.9999359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1442654165215621,"score_gpt":0.4689267416464767,"score_spread":0.3246613251249146,"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."}}