Biological Markers for Pulpal Inflammation: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Pulpitis is mainly caused by an opportunistic infection of the pulp space with commensal oral microorganisms. Depending on the state of inflammation, different treatment regimes are currently advocated. Predictable vital pulp therapy depends on accurate determination of the pulpal status that will allow repair to occur. The role of several players of the host response in pulpitis is well documented: cytokines, proteases, inflammatory mediators, growth factors, antimicrobial peptides and others contribute to pulpal defense mechanisms; these factors may serve as biomarkers that indicate the status of the pulp. Therefore, the aim of this systematic review was to evaluate the presence of biomarkers in pulpitis. METHODS: The electronic databases of MEDLINE, EMBASE, Scopus and other sources were searched for English and non-English articles published through February 2015. Two independent reviewers extracted information regarding study design, tissue or analyte used, outcome measures, results and conclusions for each article. The quality of the included studies was assessed using a modification of the Newcastle-Ottawa-Scale. RESULTS AND CONCLUSIONS: From the initial 847 publications evaluated, a total of 57 articles were included in this review. In general, irreversible pulpitis was associated with different expression of various biomarkers compared to normal controls. These biomarkers were significantly expressed not only in pulp tissue, but also in gingival crevicular fluid that can be collected non-invasively, and in dentin fluid that can be analyzed without extirpating the entire pulpal tissue. Such data may then be used to accurately differentiate diseased from healthy pulp tissue. The interplay of pulpal biomarkers and their potential use for a more accurate and biologically based diagnostic tool in endodontics is envisaged.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".