The peri‐implant mucosa color: A systematic appraisal of methods for its assessment and clinical significance
Bibliographic record
Abstract
BACKGROUND: Peri-implant mucosa color (PMC) seems to be one of the main parameters affecting the esthetic outcome of implant therapy. However, more emphasis should be given to its assessment and reporting. PURPOSE: To describe the available evidence on methods to assess and report the color of the peri-implant mucosa (PMC) and the respective clinical relevance. MATERIAL AND METHODS: A comprehensive electronic and manual search was performed to identify clinical studies reporting on PMC. RESULTS: A total of 121 studies were included. PMC was evaluated at the time of the follow-up visit (chairside) in 45.5% studies. PMC assessment was performed qualitatively, by comparing PMC with adjacent and/or contralateral gingiva (78.6%) or quantitatively, using spectrophotometry (20.7%) or a software on clinical photographs (0.8%). The most performed method to assess PMC was through esthetic indices (76.9%), either at the time of the follow-up visit (chairside) or at later time point using photographs. Quantitative reporting of PMC included averages of points from esthetic indices or color differences to natural gingiva expressed with the CIELAB color system. PMC assessment allowed describing color discrepancies compared to natural gingiva, evaluating color changes over time, and comparing the outcomes of different treatment modalities. PMC assessment through spectrophotometry was additionally utilized to assess the role of mucosal thickness (MT) on PMC. CONCLUSIONS: Various methods for PMC assessment and reporting were described, including visual assessment, mainly through esthetic indices, and spectrophotometry. PMC evaluation has allowed to demonstrate the factors affecting the color of the peri-implant soft tissue, such as the type of abutment/restoration, MT, and soft tissue augmentation.
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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 | 0.043 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.027 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".