Utilizing Oral Neutrophil Counts as an Indicator of Oral Inflammation Associated With Periodontal Disease: A Blinded Multicentre Study
Bibliographic record
Abstract
BACKGROUND: Periodontal diseases are chronic inflammatory conditions that require early screening for effective long-term management. Oral neutrophil counts (ONCs) correlate with periodontal inflammation. This study investigates a point-of-care test using a neutrophil enzyme activity (NEA) colorimetric strip for measuring periodontal inflammation. METHODS: This prospective study had two phases. Phase 1 validated the relationship between ONCs and periodontal inflammation with 90 participants. Phase 2 examined the test's applicability in a real-world setting through a multicentre clinical trial with 375 participants at four sites. ONCs were quantified in oral rinses using laboratory-based methods, and the NEA strip was used for ONC stratification. Clinical measures included bleeding on probing (BoP), probing depth (PD) and clinical attachment loss (CAL). RESULTS: ONCs were significantly elevated in patients with Grade B periodontitis and deep periodontal pockets (PD ≥ 5 mm, CAL ≥ 5 mm). The NEA strip accurately classified patients into high or low ONC categories, showing 80% sensitivity, 82.5% specificity and an AUC of 0.89. It also assessed the effectiveness of periodontal therapy in reducing ONC and inflammation. The test was user-friendly, with no reported discomfort among patients. CONCLUSION: The NEA strip is a user-friendly and rapid screening tool for detecting high ONCs associated with periodontal inflammation and for evaluating the effectiveness of periodontal therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".