A retrospective review of breast reconstruction outcomes comparing AlloDerm and DermaCELL
Bibliographic record
Abstract
Acellular dermal matrix (ADM) has become an accepted and advantageous adjunct to alloplastic breast reconstruction. The increase in demand has led to an upsurge of dermal-based products, both human and animal derived. There are few direct ADM comparative studies, but it is unclear whether there are any differences in complication rates. Our primary objective was to determine whether there is a difference in outcomes between AlloDerm and DermACELL in immediate alloplastic breast reconstruction. A retrospective chart review of those who underwent immediate alloplastic breast reconstruction from January to December 2016 was performed. This encompassed 64 consecutive patients (95 breasts) with tissue expander or direct-to-implant reconstruction and either AlloDerm or DermACELL ADM. Demographics, particulars of the surgery, additional treatments and complications were all recorded. Differences in seroma, haematoma and infection rates, as well as more serious complications including implant replacement, capsular contracture and failure, were all reviewed. The groups were comparable in terms of age, BMI and relevant comorbidities. Mastectomy weight and resulting implant volume were higher in the DermACELL group, with volume reaching statistical significance (p = 0.001). With an average follow-up of 18 months, there was no difference in capsular contraction or implant replacement. However, in those who developed capsular contracture in the DermACELL group, more breasts had no history of radiation, which was significant (p = 0.042). Overall, there were no significant differences in complication rates of seroma, haematoma, mastectomy flap necrosis and infection.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".