Clinical indices and local levels of inflammatory biomarkers in per‐implant health of obese and nonobese individuals
Bibliographic record
Abstract
BACKGROUND: Obesity seem to regulate peri-implant health. It is proposed that peri-implant crevicular fluid (PICF) levels of interleukin (IL)-1β and IL-6 are higher in obese as compared to nonobese individuals. OBJECTIVE: The purpose of the present clinico-laboratory study is to estimate and compare the clinical and radiographic indices and PICF levels of IL-1β and IL-6 among obese and nonobese patients. MATERIALS AND METHODS: individuals). Clinical indices for both periodontal and peri-implant evaluating plaque index (PI), bleeding on probing (BOP), probing depth (PD), clinical attachment level (CAL), and crestal bone loss (CBL) were recorded around teeth and implants. PICF was collected and assessed for the levels of IL-1β and IL-6 using enzyme-linked immunosorbent assay. RESULTS: A significant difference was observed in PI and BOP around natural teeth and implants in obese patients, respectively (P < .05). CBL was found to be significantly higher among obese as compared to nonobese patients (P = .022). Peri-implant and periodontal PD was higher in obese as compared to nonobese but did not reach statistical significance. Levels of IL-1β and IL-6 were statistically significantly higher among obese patients as compared to nonobese (P = .001). Pearson correlation analysis showed IL-1β was positively correlated with CBL (P = .0079), whereas IL-6 showed positive correlation with both BOP (P = .0019) and CBL (P = .015) among obese patients. CONCLUSIONS: Clinical peri-implant parameters were worse and proinflammatory biomarkers were significantly higher in obese patients compared with nonobese subjects. The findings of the present study suggests that increased proinflammatory biomarkers in PICF of obese patients may modulate peri-implant inflammation around dental implants.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".