Prognostic utility of oral neutrophil counts in high‐risk periodontitis: A retrospective study
Bibliographic record
Abstract
BACKGROUND: Oral polymorphonuclear neutrophil (oPMN) levels are potential biomarkers for differentiating between stages and grades of periodontitis. We compared the diagnostic utility of oPMN levels with bleeding on probing percentage (BOP%) and microbial load in high-risk patients with periodontitis. METHODS: Sixty-two subjects were divided into four categories based on periodontitis stage and grade: stage II periodontitis (S2P, n = 19), stage III periodontitis (S3P, n = 43), grade B periodontitis (GBP, n = 34), and grade C periodontitis (GCP, n = 28). Clinical parameters included probing depth (PD), BOP%, and clinical attachment loss (CAL). Associations between oPMN levels and BOP% were compared to periodontitis stage and grade, and the presence of the periodontal pathogens Porphyromonas gingivalis (Pg), Treponema denticola (Td), Tannerella forsythia (Tf), Aggregatibacter actinomycetemcomitans, and Prevotella intermedia (Pi) in the gingivocrevicular fluid (GCF). RESULTS: Both oPMN levels and BOP% were associated with increasing stage and grade of periodontitis; however, better sensitivity, specificity, and predictive values for differentiating between GBP versus GCP were observed with oPMN. Significant positive associations were found between oPMN level and the detection of Pg and Pi. CONCLUSIONS: OPMN level can be used to differentiate between grade B and C periodontitis. Likewise, the presence of periodontal pathogens Pg and Pi correlated with the oPMN level. Given these findings, oPMN level may be useful as a multipurpose clinical biomarker in terms of diagnosing periodontitis and determining the risk of disease progression. PLAIN LANGUAGE SUMMARY: Periodontitis is a serious gum disease that can lead to tooth loss and is linked to other health issues. Currently, bleeding of the gums after probing is one method used to assess the disease activity, but this method is not always accurate. In this study, we investigated whether counting a type of immune cell called oral neutrophil found in saliva could provide a better way to detect and measure the severity of periodontitis. We examined 62 patients with different stages and grades of the disease. We found that the number of oral neutrophils was a better tool for identifying more severe cases and those at a higher risk for future breakdown than gum bleeding. We also found that higher levels of neutrophils were linked to the presence of harmful bacteria that cause periodontitis. These findings suggest that measuring oral neutrophils could be a more reliable way to diagnose and monitor periodontitis, helping dentists identify severe cases earlier and treat patients more effectively. This method could improve how we understand and manage gum disease, leading to better patient-centered outcomes.
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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 | 0.001 | 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".