Is there any association between the presence of biomarkers and apical periodontitis? A systematic review
Bibliographic record
Abstract
This systematic review aimed to verify whether there is evidence of an association between apical periodontitis and the presence of systemic biomarkers. This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses - PRISMA. For this, the acronym PECO was used; population (P) of adult humans exposed (E) to the presence of apical periodontitis, compared (C) to adult humans without apical periodontitis, and the outcome (O) of the presence of biomarkers was observed. The articles were searched in PubMed, Scopus, Web of Science, LILACS, Cochrane Library, OpenGray, and Google Scholar grey databases. Subsequently, studies were excluded based on title, abstract, and full article reading, following the eligibility criteria. The methodological quality of the selected studies was evaluated using the Newcastle-Ottawa qualifier. After exclusion, 656 studies were identified, resulting in 17 final articles that were divided into case-control, cross-sectional, and cohort studies. Eight studies were considered to have a low risk of bias, one had a medium risk of bias, and eight had a high risk of bias. In addition, 12 articles evaluated biomarkers in blood plasma, four evaluated them in saliva, and only one evaluated them in gingival crevicular fluid. The results of these studies indicated an association between apical periodontitis and the systemic presence of biomarkers. These markers are mainly related to inflammation, such as interleukins IL-1, IL-2, and IL-6, oxidative markers, such as nitric oxide and superoxide anions, and immunoglobulins IgG and IgM. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier (CRD42023493959).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".