Cosmetic and functional outcomes after preoperative tissue expansion of radial forearm free flap donor sites: a cohort study.
Bibliographic record
Abstract
INTRODUCTION: The use of the DynaClose topical tissue expansion device for closure of radial forearm free flap (RFFF) donor sites has been demonstrated to significantly reduce healing time and postoperative pain compared to the traditional use of a split-thickness skin graft. However, long-term cosmetic and functional outcomes are not known. OBJECTIVE: The objective of this study was to test the hypothesis that using a new method of donor site management will result in improved cosmesis of RFFF donor sites as assessed by both patients and expert observers without resulting in a reduction in the function of the patients' forearm. METHODS: A cohort of 25 patients previously randomized to either the treatment (tissue expansion) or the control group were assessed at 10 months. The Patient and Observer Scar Assessment Scale (PAOSAS) was used to assess forearm scars, whereas the Michigan Hand Outcomes Questionnaire assessed hand function. RESULTS: Expert observers noted improved scar cosmesis in the treatment group (p = .013), with primary closure having the best cosmetic outcome, followed by local full-thickness skin graft closure (p < .001). There was no statistically significant improvement in cosmesis as assessed by patients (p = .03) or differences in Michigan Hand Outcomes Questionnaire scores between treatment groups (p = .57). CONCLUSION: Using an inexpensive, noninvasive preoperative tissue expansion device safely results in improved cosmetic outcomes as assessed by expert observers, without any significant functional forearm and hand deficits.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".