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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".