A Review of Tissue Expansion-Assisted Techniques of Cleft Palate Repair
Bibliographic record
Abstract
BACKGROUND: The purpose of this report was to examine current knowledge of use of tissue expansion techniques to assist cleft palate repair and to review and contrast various techniques reported. METHODS: Two separate literature searches were conducted in the Cochrane Library, CINAHL, Medline and Embase databases, from database inception until December 2014 for use of mucoperiosteal expansion (MPE) and distraction osteogenesis (DO) in cleft palate repair. RESULTS: Six articles, reporting a total of 51 patients of palatal MPE, were identified for discussion and analysis. Three different MPE techniques in primary cleft palate repair were described: intraoperative rapid expansion, tumescent injections, and a 2-stage repair with an osmotic expander. Average fistula rate was 26.19%. The search for use of DO on palatal clefts revealed 6 animal models, a finite element analysis study, and 1 case report. Moreover, 2 patients were reported of the use of DO to assist in secondary palatal fistula closure. No fistula rate could be calculated due to the heterogeneity of the data. CONCLUSIONS: The experience with MPE in assisting cleft palate repair remains limited. Among expansion techniques, the use of osmotic expanders was associated with the highest rates of postoperative fistulae. The literature provides little evidence supporting the efficacy of MPE expansion in cleft palate repair. The majority of studies utilizing DO to assist primary cleft palate repair are in animal models with the exception of isolated case reports in human subjects. Although limited, the results demonstrate promise and the need for further research in this domain.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".