Peri‐implant parameters, tumor necrosis factor‐alpha, and interleukin‐1 beta levels in vaping individuals
Bibliographic record
Abstract
BACKGROUND: To the author's knowledge, there has been no study that has assessed clinical, radiographic, and immunological peri-implant parameters among individuals vaping e-cigarette (e-cig). PURPOSE: This pilot study aimed to compare clinical and radiographic peri-implant parameters and levels of tumor necrosis factor alpha (TNF-α) and interleukin (IL)-1β levels among individuals vaping e-cigs and never smoker (NS). MATERIALS AND METHODS: Forty-seven individuals vaping e-cigs (group-1) and 45 NS (group-2) were included. Demographic and implant-related data were collected using a structured baseline questionnaire. Peri-implant plaque index (PI), bleeding on probing (BOP), and probing depth (PD) were recorded and peri-implant bone loss (PIBL) were assessed using standardized digital radiographs. Enzyme-linked immunosorbent assay was used to assess the levels of TNF-α and IL-1β in peri-implant sulcular fluid. RESULTS: Bleeding on probing showed statistically significantly higher values in group-2 patients as compared to group-1 patients (P < .01). Probing depth ≥ 4 mm and PIBL was statistically significantly higher in group-1 patients as compared to group-2 patients (P < .05). Mean concentrations of TNF-α (P < .001) and IL-1β (P < .01) were statistically significantly increased in individuals in group 1 as compared with group 2. A significant positive correlations were found between TNF-α levels and BOP (P = .024) and PIBL (P = .016); and significant positive correlation was found between IL-1β and PIBL (P = .018) in group 1, respectively. CONCLUSIONS: Clinical and radiographic peri-implant parameters are compromised among vaping individuals. Increased levels of proinflammatory cytokines in peri-implant sulcular fluid may suggest greater local inflammatory response in vaping individuals for peri-implant inflammation.
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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 | 0.000 | 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 | 0.000 |
| 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".