Changes and Roles of IL-6, hsCRP, and proCT in Patients with Chronic Periodontitis in Head and Neck Cancer Pre/Post Radiotherapy
Bibliographic record
Abstract
Background: Head and neck cancer (HNC) patients frequently undergo radiotherapy as a standalone treatment or in combination with chemotherapy. Radiotherapy is associated with adverse effects, including detrimental impacts on periodontal health, which increase the risk of periodontitis. Objective: To investigate the clinical significance of interleukin-6 (IL-6), high-sensitive C-reactive protein (hsCRP), and procalcitonin (proCT) as prognostic indicators. Methods: 150 participants were divided into three groups: (n=50, HNC post-RT) patients with head and neck cancer who had radiation treatment six months ago (n=50, HNC pre-RT), and individuals with periodontal health as the control group (n=50). Probing pocket depth (PPD), clinical attachment loss (CAL), gingival bleeding index (GBI), plaque index (PI), and hyposalivation were meticulously recorded. To quantify serum concentrations of IL-6, hs-CRP, and proCT, an electrochemiluminescence immunoassay (eCLIA) was used. Results: Serum levels of IL-6, hsCRP, and proCT were significantly elevated in two groups of patients with chronic periodontitis with head and neck cancer post-radiotherapy (CP+HNC post-RT) and patients with chronic periodontitis with HNC pre-radiotherapy (CP+HNC pre-RT) compared to a control group. ROC analysis demonstrated the diagnostic accuracy of IL-6, hsCRP, and proCT for both clinical cases. Furthermore, all clinical periodontal index scores (CAL, PPD, PI, and GBI) were significantly elevated compared to a control group. Conclusions: HNC post-RT patients presented significantly higher serum IL-6, hs-CRP, proCT, and periodontal score levels than HNC pre-RT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".