{"id":"W3011155732","doi":"10.1371/journal.pone.0223461","title":"Salivary molecular spectroscopy: A sustainable, rapid and non-invasive monitoring tool for diabetes mellitus during insulin treatment","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Canadian Institutes of Health Research; Universidade Federal de Uberlândia; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Instituto Nacional de Ciência e Tecnologia em Teranóstica e Nanobiotecnologia","keywords":"Diabetes mellitus; Saliva; Medicine; Fourier transform infrared spectroscopy; Internal medicine; Insulin; Spectroscopy; Chemistry; Gastroenterology; Endocrinology; Physics; Optics","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":[0.001179131,0.000738124,0.000752295,0.001306051,0.0002527122,0.0009472183,0.000435739,0.0008903591,0.0005977235],"category_scores_gemma":[0.0008881586,0.0002802761,0.000559664,0.001024625,0.0003052965,0.0007490006,0.0005163867,0.0009475207,0.0004696283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002138835,"about_ca_system_score_gemma":0.0003193634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005684071,"about_ca_topic_score_gemma":0.0006423004,"domain_scores_codex":[0.999184,0.0001771769,0.00004774261,0.0001684182,0.0003734854,0.00004918771],"domain_scores_gemma":[0.9995748,0.00007506758,0.0001687818,0.00002613865,0.0001273792,0.00002779273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004657032,0.0002076021,0.02577268,0.001188309,0.0001553472,0.0002409559,0.0001664433,0.0006548124,0.7997275,0.0003464342,0.001681852,0.1693924],"study_design_scores_gemma":[0.00006565312,0.002168926,0.1772233,0.0004305577,0.0008071041,0.003802325,0.0008208922,0.02686144,0.7567028,0.001739306,0.02909499,0.0002827363],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7362142,0.1082914,0.1406575,0.003293636,0.0007779745,0.0002244535,0.001978429,0.001796638,0.006765877],"genre_scores_gemma":[0.8902608,0.03529651,0.06864967,0.0008907188,0.0003636145,0.0001373888,0.0006874125,0.00008195187,0.003632048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001306051,"threshold_uncertainty_score":0.006235898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01775701910615539,"score_gpt":0.2644899905947836,"score_spread":0.2467329714886282,"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."}}