Oral inflammatory load: Neutrophils as oral health biomarkers
Bibliographic record
Abstract
Periodontal diseases present a significant challenge to our healthcare system in terms of morbidity from the disease itself as well as their putative and deleterious effects on systemic health. The current method of diagnosing periodontal disease utilizes clinical criteria solely. These are imprecise and are somewhat invasive. There is thus significant benefit to creating a non-invasive test as a method of screening for and monitoring of periodontal diseases, and, in particular, chronic periodontitis. Oral polymorphonuclear neutrophil (oPMN) counts have been found to correlate with extent of oral inflammation and the presence and severity of periodontal diseases. Potentially then, quantification of oPMNs might be used to identify and measure the severity of oral inflammation (oral inflammatory load; OIL) in subjects with healthy and inflamed periodontal tissues, demonstrating a positive correlation between higher oPMN counts and the extent/severity of OIL. These findings support the development and utilization of a non-invasive chair-side test enabling rapid, accurate, and objective screening of OIL based on measurement of oPMN numbers (similar to white blood cell levels in blood as used in medicine for assessment of infection). The use of such a test before, during, and after treatment of gingivitis and periodontitis could lead to improvements in timing of intervention (ie, when inflammation is active) thereby reducing long-term morbidity.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".