Peri‐Implant Health and Perfusion Parameters in Patients After Microvascular Jaw Reconstruction: A Clinical Cohort Study
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to evaluate perfusion parameters and clinical features of healthy implants and implants affected by peri-implant disease in patients who had undergone microvascular jaw reconstruction. METHODS: A total of 25 patients with 92 implants placed in microvascular transplants were included. Of these, 68 implants showed healthy peri-implant tissue, 12 were affected by peri-implant mucositis, and 12 were diagnosed with peri-implantitis. Peri-implant perfusion was measured mesially and distally at the implant shoulder using laser Doppler flowmetry and tissue spectrophotometry (LDF-TS), followed by a clinical evaluation, including measurement of probing depths, bleeding on probing (BOP), plaque index, biotype, type of implant, the restoration and the presence of keratinized tissue. Perfusion parameters were compared between the healthy implants and the implants with peri-implant disease based on the conventional BOP-based diagnosis of peri-implantitis, and the associations between the perfusion values and clinical measurements were analyzed. Optimal cut-off values for predicting peri-implantitis were calculated with receiver operating characteristics. RESULTS: The mean relative amount of hemoglobin and mean blood flow were significantly different between healthy implants and implants with peri-implant mucositis and peri-implantitis (p = 0.003 and p = 0.002, respectively). However, there are interindividual differences that appear to influence blood flow values as well. When a linear mixed regression model was applied, including the patient as a random variable, the difference in blood flow was no longer statistically significant (p = 0.400). Still, the optimal cut-off value of mean blood flow for predicting peri-implantitis was determined to be > 46.5 AU (AUC = 0.788; p < 0.001; CI = 0.695-0.881; sensitivity = 1.00, specificity = 0.60). CONCLUSION: Implants in microvascular flaps are particularly vulnerable to peri-implant disease. Risk factors are the lack of keratinized peri-implant tissue, fixed restorations, bone-level implants, and high plaque levels. As a noninvasive and objective method, LDF-TS can contribute to risk assessment by evaluating perfusion parameters and help detect the early onset of peri-implant disease.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".