Longitudinal Comparison of Cytokines in Peri‐Implant Fluid and Gingival Crevicular Fluid in Healthy Mouths
Bibliographic record
Abstract
BACKGROUND: Peri-implant and gingival tissues provide important sealing and protective functions around implants and teeth, but comparisons of the immunologic responses of these tissues after implant placement have not been conducted. Cytokine levels were measured in peri-implant crevicular fluid (PICF) and gingival crevicular fluid (GCF) as surrogate measures of immune function at subcrestally placed dental implants and healthy periodontal sites during a 1-year monitoring period. METHODS: A total of 27 dental implants were placed subcrestally in 21 periodontally healthy patients (mean age: 49.0 ± 13.4 years). Repeated clinical and cytokine measurements were obtained over 12 months. GCF and PICF samples were collected and analyzed by cytokine microarray. Data were examined by non-parametric analysis of variance. RESULTS: Plaque and bleeding indices were similar among all patients (P >0.05) at baseline. During 1 year of monitoring, the mean volumes of PICF and GCF were similar (P >0.05). The levels of interleukin (IL)-4, -6, -10, and -12p70, tumor necrosis factor-α, and interferon-γ in GCF and PICF were not significantly different and did not vary over time (P >0.05). The levels of IL-1α were higher in GCF than PICF at 1, 2, 6, and 12 months, as were the levels of IL-8 at 1, 2, 4, 6, and 12 months (P <0.001). Transforming growth factor-β1 in PICF and GCF exhibited time-dependent increases, and vascular endothelial growth factor was reduced at 1 year without differences between PICF and GCF (P >0.05). CONCLUSION: Within the limitations of this study design, it can be concluded that after subcrestal implant placement, the immune response of peri-implant and periodontal tissues, as assessed by cytokine levels in PICF and GCF, is similar.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".