Acellular Dermal Matrix in Cleft Palate Repair
Bibliographic record
Abstract
BACKGROUND: The repair of wide cleft palates and secondary palatal fistulas remains a challenge for pediatric plastic surgeons. To reduce the incidence of fistulization, use of acellular dermal matrix to facilitate closure has been reported in the literature. A review of the literature was performed to assess whether sufficient evidence exists to recommend the routine use of acellular dermal matrix for either primary palatoplasty or secondary palatal fistula repair. METHODS: A literature search for the period between 1970 and 2011 was performed. All articles with clinical application of acellular dermal matrix in primary palatoplasty or palatal fistula repair were included. Data were analyzed using weighted averages to compare fistula rates between repairs performed with and without acellular dermal matrix (historical controls) for each repair type (primary versus secondary fistula repairs). RESULTS: Four studies examined the use of acellular dermal matrix in primary palatoplasty (n = 92) with a mean cleft width of 14.2 mm. The overall fistula rate was 5.4 percent compared with 10.6 percent in the non-acellular dermal matrix historical control group. Five studies used acellular dermal matrix in palatal fistula repair (n = 74). The overall recurrent fistula rate was 8.1 percent compared with 12.9 percent in the historical control group. CONCLUSIONS: Based on the available data, the results imply that acellular dermal matrix may have a potential benefit in reducing fistula formation/persistence in palate surgery. However, the authors did not find sufficient prospective randomized (level II or better) evidence to recommend the routine use of acellular dermal matrix for cleft palate repair.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".