A Prospective Study Investigating Fistula Rate Following Primary Palatoplasty Using Acellular Dermal Matrix
Bibliographic record
Abstract
Background: Acellular dermal matrix (ADM) has been described as an adjunct in primary cleft palate repair to reduce the fistula rate in several retrospective studies (level III or lower); however, current data are insufficient to definitively conclude its efficacy for this purpose. The goal of the present study was to provide prospective, higher level of evidence data investigating the effect of ADM on fistula rate following primary palatoplasty. Methods: A prospective clinical trial was conducted in which ADM was used uniformly in all primary cleft palate repairs that met inclusion criteria. For comparison, a matched control group was identified (retrospectively) from the same center/surgeon’s database. Primary outcome was the rate of palatal fistula formation. Secondary outcomes included bleeding, infection, and delayed healing. Results: A total of 130 patients were included in the analysis consisting of 65 in both the study and control groups. There were no statistically significant differences in patient demographics or cleft /surgical characteristics. The results demonstrated a fistula rate of 1.5% in the study group versus 12.3% in the control group ( P = 0.03). The other complications (infection, bleeding, delayed healing) were similar between the groups. Conclusion: The study provides the highest level of evidence currently available (level II, prospective data) investigating the effect of ADM on fistula rate following primary palatoplasty. The results demonstrate a low overall fistula rate (1.5%) and suggest there may be a clinically significant reduction in fistula formation associated with the routine use of ADM in all primary palate repairs.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".