Evaluation of FGF‐23 and 25(OH)D<sub>3</sub> levels in peri‐implant sulcus fluid in peri‐implant health and diseases
Bibliographic record
Abstract
Abstract Background There are limited studies to date investigating vitamin D and fibroblast growth factor (FGF)‐23 in different peri‐implant conditions. Purpose To evaluate the peri‐implant sulcus fluid (PISF) FGF‐23 and 25‐hydroxy‐vitamin D 3 (25(OH)D 3 ) levels in peri‐implant health and diseases. Materials and Methods A total of 90 dental implant sites (peri‐implant healthy group [n = 30], peri‐implant mucositis group [n = 30], and peri‐implantitis group [n = 30]) in 53 participants were included in the study. Probing depth (PD), clinical attachment level (CAL), suppuration (S), modified plaque index (mPI), gingival index (GI), modified sulcus bleeding index (mSBI), and keratinized mucosa width (KMW) were recorded as clinical parameters, and PISF samples were obtained. FGF‐23 and 25(OH)D 3 levels were analyzed by enzyme‐linked immunosorbent assay. Results There were no statistically significant differences in FGF‐23 concentrations among the groups ( P > .05). The 25(OH)D 3 concentration was significantly lower in peri‐implantitis group compared with the other two groups ( P < .05). The mean total amount of FGF‐23 in the peri‐implantitis group was significantly higher than the peri‐implant healthy group whereas 25(OH)D 3 total amount was significantly lower in the peri‐implantitis group than the peri‐implant healthy group. The 25(OH)D 3 concentration was significantly negatively correlated with CAL, PD, mPI, S, GI, and mSBI and statistically significant relationship was found between FGF‐23 total amount and these clinical parameters ( P < .05). There was a negligible positive correlation between 25(OH)D 3 and FGF‐23 concentrations (τ = 0.169; P = .018). Conclusion Within the limitations of this study, it can be concluded that FGF‐23 and vitamin D seems to affect peri‐implant bone health, and further studies are needed to explain the association between FGF‐23 and 25(OH)D 3 in peri‐implant conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".