Soft tissue grafting in healing chronic wounds: Two case studies
Bibliographic record
Abstract
BACKGROUND: A chronic wound occurs when it fails to heal resulting in a prolonged wound healing process. Traumatic and surgically induced wounds that do not heal as expected can be included in the term chronic wound. There is limited information about the management of this type of wound involving the periodontal tissues. These case studies show how soft tissue grafting may be used as a strategy to assist in the closure and enhancement of large wounds that present a chronic and complicated healing process. METHODS AND RESULTS: Two healthy adult patients presented open wounds induced by trauma following the dental treatment. These wounds were treated with autogenous soft tissue grafting to enhance and stimulate the healing process. In one case, enamel matrix derivative was applied in conjunction with a gingival graft. Both cases demonstrated a successful and rapid wound closure at the 1-week post-treatment. CONCLUSION: Using autogenous soft tissue grafting should be considered a treatment alternative when dealing with a chronic and problematic open wound. This study describes the success of two cases utilizing this approach. However, an understanding of the biological process of wound healing is critical when selecting a surgical technique for the treatment of a chronic wound. KEY POINTS: There is limited information in the periodontal literature about the management of chronic wounds caused by trauma. Critical understanding of the biological process of wound healing and application of surgical principles are essential in using autogenous soft tissue grafts to treat these wounds. Surgical factors such as extent, location of wound, and limited amount of autogenous donor tissue are primary limitations to the successful treatment of these cases. PLAIN LANGUAGE SUMMARY: Two cases that presented chronic wounds related to the periodontal tissues which were the result of surgical trauma are reported. These complicated healing wounds were managed with free autogenous soft tissue grafts. The grafts assisted the problematic wounds in healing by replacing missing tissue and providing growth factors. This mode of treatment for a chronic wound proved to be a successful approach in the management of this type of chronic wound.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".