{"id":"W4399680343","doi":"10.1111/jre.13313","title":"Progression from healthy periodontium to gingivitis and periodontitis: Insights from bioinformatics‐driven proteomics – A systematic review with meta‐analysis","year":2024,"lang":"en","type":"review","venue":"Journal of Periodontal Research","topic":"Oral microbiology and periodontitis research","field":"Dentistry","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"KEGG; Gingivitis; Periodontitis; Saliva; Downregulation and upregulation; Aggressive periodontitis; Periodontium; Proteomics; Bioinformatics; Biology; Medicine; Computational biology; Dentistry; Gene ontology; Gene; Genetics; Gene expression; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003713306,0.001267897,0.01277054,0.003254957,0.0006954735,0.001900079,0.002074485,0.0009348776,0.000939223],"category_scores_gemma":[0.001001164,0.0007407634,0.003757996,0.003782592,0.0006776752,0.0007712066,0.001320519,0.004711999,0.0008855451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008058255,"about_ca_system_score_gemma":0.002162091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001142159,"about_ca_topic_score_gemma":0.003942383,"domain_scores_codex":[0.986924,0.003876106,0.003904375,0.00125968,0.002769618,0.001266239],"domain_scores_gemma":[0.9931104,0.001168162,0.001735793,0.00137479,0.001426341,0.001184575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0003561943,0.0002383232,0.000047361,0.8230598,0.1578148,0.004807562,0.002064515,0.000001144432,0.00004704771,0.00002545386,0.003598843,0.007939012],"study_design_scores_gemma":[0.0006211098,0.00230933,0.00004658126,0.5107957,0.347071,0.002511359,0.0008999922,0.00004197586,0.00001082105,0.00002320348,0.1343956,0.001273314],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001735077,0.9885135,0.0001297779,0.0003941028,0.0004414103,0.006340366,0.002299668,0.00005297477,0.00009308742],"genre_scores_gemma":[0.0003526121,0.9897513,0.006982421,0.0001426098,0.0004310811,0.0009585092,0.0004584327,0.0001951194,0.0007278817],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.3122641,"threshold_uncertainty_score":0.9999741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.112104659009843,"score_gpt":0.4449505494996944,"score_spread":0.3328458904898515,"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."}}