Mandibular Anterior Lingual Recession: Keratinized Tissue Grafting and Minimally Invasive Harvesting
Bibliographic record
Abstract
INTRODUCTION: The mandibular anterior lingual (MAL) keratinized tissue (KT) band is often insufficient in dimension: <2 mm height of which <1 mm is attached gingiva (AG). Its gingival phenotype is commonly characterized as thin (<1 mm) gingival thickness (GT) and having inadequate (<1 mm) AG width. When surgical treatment is indicated, prevention of significant apical displacement of the gingival margin and improvement of long-term gingival stability are enhanced by KT increase and phenotype modification in order to establish thick GT and adequate AG. The aim of this case report is to describe a bilaminar surgical approach, the modified coronally advanced flap (mCAF) and connective tissue graft with retained KT band (mCAF + CTGkt). It is an outcomes-driven surgical approach for KT increase and phenotype modification in order to predictably establish thick GT and adequate AG. The mCAF + CTGkt procedure is minimally invasive, predictable, well-tolerated and addresses both the unique features of MAL anatomy and normal oral functioning movement during the postoperative healing phase. CASE PRESENTATION: A 48-year-old female presented with chief complaint of MAL progressive gingival recession (GR). Attachment loss of 3-4 mm and lack of both KT and AG were documented. Primary treatment outcomes objectives were GR cessation, establish KT, increase GT and AG. A secondary outcome was decreasing GR. CONCLUSION: The mCAF + CTGkt procedure resulted in KT increase, phenotype modification to establish thick GT and adequate AG, and decreased GR. It addressed unique features of MAL anatomy. Postoperative healing outcomes were not negatively impacted by normal oral functioning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".