Does Size Matter? Technical Considerations of a Regenerative Tissue Matrix for Use in Reconstructive Surgery
Bibliographic record
Abstract
INTRODUCTION: Acellular dermal matrices have been used with increasing frequency in both reconstructive and cosmetic surgery. While many studies have described the safety and morbidity profiles of these materials, little is known about the relative mechanical properties of individual sheets of allograft harvested from distinct donors. METHODS: Sixty-two individual sheets of an acellular dermal matrix from distinct lot numbers (signifying different donors of the dermis) were prospectively analyzed before use. Distribution of thickness according to manufacturer specifications in the dry state were as follows: 0.009 inches to 0.013 inches (1 [1.6%]); 0.79 mm to 1.78 mm (3 [4.8%]); 0.79 mm to 2.03 mm (5 [8%]); 0.8 mm to 3.3 mm (1 [1.6%]); 1.8 mm to 3.3 mm (10 [16.1%]) and 28 mm (6 [9.7%]). The size of the matrix was recorded while dry, after hydration and following stretch. The percentage change in surface area was recorded for each lot. RESULTS: The 62 reconstructive cases included breast implant reconstruction (2 [3.2%]); ventral hernia repair (11 [17.7%]); abdominal closure following autologous tissue harvest (6 [9.6%]); autologous breast reconstruction (37 [59.6%]); extremity wound closures (3 [4.8%]) and reinforcement of vertical rectus abdominis muscle closure (3 [4.8%]). The mean percentage change in the size of the acellular dermal matrix to the hydrated state was 58% (36 of 62; thickness 0.06 mm to 3.30 mm); the mean percentage change in size from dry state was 7.14% (range 0% to 18.7%). The mean percentage change in the size of the hydrated matrix to the stretched state was 25.7% (range 0.25% to 70.6%). The variability in elasticity among the individual sheets was significant (P<0.0005). CONCLUSION: The acellular dermal matrix displayed highly variable elastic properties among distinct donors. This may be significant in procedures in which symmetry is critical.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".