Postoperative Mechanomodulation Decreases T-Junction Dehiscence After Reduction Mammaplasty: Early Scar Analysis From a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Soft tissue and cutaneous tension is an important contributor to complicated wound healing and poor scar cosmesis after surgery and its mitigation is a key consideration in aesthetic and reconstructive procedures. OBJECTIVES: The study objective was to assess the efficacy of the force modulating tissue bridge (FMTB) ("Brijjit", Brijjit Medical Inc., Atlanta, GA) in reducing mechanical tension on postoperative wounds. METHODS: A prospective, single-center, randomized, within-subject clinical trial was conducted to evaluate wound healing and nascent scar formation after 8 weeks of postoperative wound support with the FMTB. Patients received standard of care (SOC) subcuticular closure on the vertical incision of 1 breast and experimental closure with the FMTB on the contralateral incision after Wise-pattern reduction mammaplasty. Three-dimensional wound analysis and rates of T-junction dehiscence were evaluated by clinical assessment at 2, 4, 6, and 8 weeks postsurgery. RESULTS: Thirty-four patients (n = 68 breasts) completed 8 weeks of postoperative FMTB application. There was a reduced rate of T-junction wound dehiscence in FMTB breasts (n = 1) vs SOC breasts (n = 11) (P < .01). The mean vertical incision wound area during the intervention period was significantly decreased in the FMTB breast (1.5 cm2) vs the SOC breast (2.1 cm2) (P < .01) and was significantly lower at 2-, 4-, and 8-week follow-up (P < .01). Only the closure method was significantly associated with variations in Week 8 wound area (P < .01) after linear regression modeling. CONCLUSIONS: FMTBs decrease nascent scar dimensions and reduce the occurrence of wound dehiscence. This study provides evidence that the use of continuous mechanomodulation significantly reduces postoperative wound complications after skin closure.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".