The Evidence Base for the Acellular Dermal Matrix AlloDerm
Bibliographic record
Abstract
BACKGROUND: Many decellularized dermal matrices are available with various applications, all with slight differences. AlloDerm appears to have the greatest presence in the literature. The purpose of this systematic review is to provide an overview of the experience with AlloDerm, stratified by clinical indication. METHODS: A literature search was performed across Medline, EMBASE, and the Cochrane Collaboration using the search terms "AlloDerm" and "acellular dermal matrix." Two independent authors applied a priori inclusion and exclusion criteria. Relevant articles were categorized by application, type of study, and evidence level. RESULTS: A total of 753 articles met the initial inclusion criteria, and 311 remained after discarding irrelevant articles: skin (25), head and neck (82), breast (34), trunk (66), pelvis (10), extremities (8), and basic science (86). Non-basic science study designs included 32 analytic articles (3 randomized controlled trials and 29 observational studies including 11 cohort studies, 1 cross-sectional study, and 17 case-controlled studies), 192 descriptive articles (106 case series, 51 case reports, 2 cross-sectional studies, and 33 qualitative studies), and 1 systematic review. More than 85% of articles had a level of evidence of 4 or 5. Articles showed outcomes that were 70% positive, 23% neutral, and 7% negative. CONCLUSIONS: AlloDerm has many clinical uses with promising results. Most evidence lies in descriptive and nonrandomized studies, but randomized trials are emerging. Cost and logistics of large trials with these products make large-scale trials challenging but necessary. Emphasis needs to shift to randomized controlled trials focusing on areas where most clinical benefit can be realized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.025 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".