Myeloperoxidase as a Measure of Polymorphonuclear Leukocyte Response in Inflammatory Status Around Immediately and Delayed Loaded Dental Implants: A Randomized Controlled Clinical Trial
Bibliographic record
Abstract
BACKGROUND: As well as gingival crevicular fluid (GCF), peri-implant sulcus fluid (PISF) may have a potential diagnostic value for the early identification of metabolic and destructive processes. PURPOSE: The aim of this study was to analyze the potential impact of inflammation and loading on PISF myeloperoxidase (MPO) levels, in comparison with GCF. MATERIALS AND METHODS: A total of 220 sites, dental implant (immediately [IL] or delayed loaded [DL]), and natural tooth, either healthy/noninflamed or gingivitis/inflamed, were classified. Clinical parameters were recorded, and GCF/PISF samples were obtained. GCF/PISF MPO levels were spectrophotometrically determined. RESULTS: Clinical parameters demonstrated increases with the presence of gingival/peri-implant inflammation. Total MPO levels were higher at inflamed tooth and implant sites compared to noninflamed/healthy sites (p < .05). Although they did not reach a significance level, inflamed IL sites had higher total MPO levels than inflamed DL sites (p = .401). Gingival index and total MPO levels exhibited significant correlations (p < .05). CONCLUSION: Using implants and natural teeth in the same study design, the findings of the present study support the close relationship between MPO production and inflammation, and may speculate a potential for loading of dental implants, contributing to the MPO content of PISF.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".