Repair of Primary Cleft Palate and Oronasal Fistula With Acellular Dermal Matrix: A Systematic Review and Surgeon Survey
Bibliographic record
Abstract
OBJECTIVE: The current review and survey aim to assess the effectiveness of acellular dermal matrix (ADM) in the repair of cleft palate and oronasal fistula and to evaluate the current trends of ADM use in palate surgery. DESIGN: A systematic review of English articles was conducted using MEDLINE (1960 to July 1, 2016), the Cochrane Controlled Trials Register (1960 to July 1, 2016), and EMBASE (1991 to July 1, 2016). Additional studies were identified through a review of references cited in initially identified articles. Search terms included "cleft palate," "palatal," "oronasal fistula," "acellular dermal matrix," and "Alloderm®." An online survey was disseminated to members of the American Cleft Palate-Craniofacial Association to assess current trends in ADM use in palate surgery. STUDY SELECTION: All studies evaluating the outcome of primary palate repair or repair of oronasal fistula with the use of aceullar dermal matrix products were included in the review. RESULTS: Twelve studies met inclusion criteria for review. Studies were generally of low quality, as indicated by methodological index for non-randomized studies (MINORS) scores ranging from 7 to 14. The pooled estimate for fistula formation after primary palatoplasty following ADM use was 7.1%. The pooled estimate for recurrence of fistula after attempted repair using ADM was 11%. Thirty-six cleft surgeons responded to the online survey study. Of these, 45% used ADM in primary cleft palate repair, while 67% used ADM for repair of oronasal fistulae. CONCLUSION: Use of ADM products is commonplace in palate surgery. Despite this, there is a paucity of high-quality data demonstrating benefit. Further randomized controlled trials examining ADM in palate surgery are required to help develop structured guidelines and improve care.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.014 |
| 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) | 0.003 | 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".