550 A Systematic Review of Aesthetic Outcome Using Dermal Regeneration Templates in Nasal Reconstruction
Bibliographic record
Abstract
Abstract Aim Nasal reconstruction using Dermal Regeneration Templates (DRT) has been proposed due to their ability to reduce contour defects, avoid donor site morbidity, and facilitate reconstruction. This review aims to determine the aesthetic outcomes of DRT's in nasal reconstruction. Method A full systematic literature review of DRT use in nasal reconstruction was performed by two authors in December 2021. Cochrane Central Register of Controlled Trials, Ovid EMBASE and Ovid MEDLINE were searched and included papers reporting objective measures of aesthetic outcome. Of the 385 papers identified, 5 were included. Results 75 patients were included. Defects resulted from excision of skin tumors (74) and vascular malformation (1). 1 study used Hyalomatrix, 1 used Integra or Matriderm and 3 used Integra only. 2 papers using Integra left the defect to heal secondarily without a skin graft and healing time was 6 weeks or less, while 3 studies performed a second stage with a full thickness skin graft at 2 weeks or later. Mean defect size was poorly reported, and several subunits were reconstructed. Using the Vancouver Scar Scale and Visual Analogue Cosmetic Scale, significant improvements compared to pre-operative assessment were observed and good to high patient satisfaction. Complications were infrequent. Subsequent flap reconstruction was performed in 12 patients. Conclusions Nasal reconstruction using DRT is reliable however data on aesthetic outcomes are limited. The re-operation rate is reasonably high and data comparing aesthetic outcomes to flaps and skin grafts are lacking. More research reporting objective outcomes is warranted to further evaluate the utility of DRT's.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".