Bibliographic record
Abstract
Objectives: Innovative methods for pulpal diagnosis are becoming increasingly important in endodontics, as traditional diagnostic techniques often lack the precision and reliability needed for confident decision making. Molecular diagnostic approaches, like biomarker analysis and advanced sampling methods, represent a step forward in clinical practice. This narrative review aims to identify key biomarkers associated with pulpal inflammation, compare published cutoff points for these biomarkers, and briefly review molecular methods and sampling techniques. Search Strategy: This study is a narrative review of literature identified by a web-based search on PubMed. Original scientific articles, such as clinical studies, reviews, and case reports, were included. Results: Several biomarkers have been associated with pulp inflammation and have been reported as statistically significant, including interleukin 1, 6, and 8; tumor necrosis factor-α; vascular endothelial growth factor; fibrocyte growth factor acidic; and matrix metalloproteinases 8 and 9. Although some cutoff points for these biomarkers have been identified, further research is necessary to refine their clinical applicability. Various sample collection methods, such as gingival crevicular fluid, dentinal fluid, pulpal tissue, and pulpal blood, have been used. Among the analytical techniques, enzyme-linked immunosorbent assay and Luminex protein assays have proven to be the most accurate, sensitive, and specific for evaluating pulpal inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".