A Randomized Controlled Trial Comparing Alloderm-RTU with DermACELL in Immediate Subpectoral Implant-Based Breast Reconstruction
Bibliographic record
Abstract
BACKGROUND: The effectiveness of different acellular dermal matrices (ADM) used for implant-based reconstruction immediately following mastectomy is an important clinical question. A prospective randomized clinical trial was performed to evaluate the superiority of DermACELL over Alloderm-RTU in reducing drain duration. METHODS: Patients undergoing mastectomy with subpectoral immediate and permanent implant-based breast reconstruction were randomized to Alloderm-RTU or DermACELL. The primary outcome was seroma formation, measured by the duration of postoperative drain placement. Secondary outcomes included: post drain removal seroma aspiration, infection, redbreast syndrome, wound dehiscence, loss of the implant, and unplanned return to the operating room. RESULTS: = 0.16), with a trend towards longer duration in the Alloderm-RTU group (1.6 days; 95%CI, 0.7 to 3.9). The overall rate of minor and major complications were statistically similar between the two groups; although patients with Alloderm-RTU had 3 times as many infections requiring antibiotics (7.9% vs. 2.5%) with a risk difference of 5.4 (95%CI -4.5 to 15.2), and twice as many unplanned returns to the operating room (15.8% vs. 7.5%) with a risk difference of 8.3 (95% CI -5.9 to 22.5) as DermACELL. CONCLUSION: This is the first prospective randomized clinical trial comparing the two most commonly used human-derived ADMs. There was no statistically significant difference in drain duration, minor, or major complications between DermACELL over Alloderm-RTU in immediate subpectoral permanent implant-based breast reconstruction post-mastectomy.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".