Destination Design msTRAM: For Greater Reconstructive Certainty
Bibliographic record
Abstract
Background: Performing delayed reconstruction to a unilateral breast while simultaneously performing a balancing procedure on the contralateral side can be the most difficult situation to achieve symmetry. We present here a novel approach to free TRAM-based breast reconstruction using reverse planning and subunit principles with simultaneous balancing reduction mastopexy and immediate nipple reconstruction. Methods: A retrospective chart review and a BREAST-Q questionnaire of a single surgeon’s practice was performed to compare revision rates and patient satisfaction following Destination Design msTRAM reconstruction compared with a historical cohort of patients who received traditional free TRAM reconstruction. Results: The chart review identified 39 patients treated with the traditional unilateral technique from 1997 to 2004 and 88 patients treated with the novel unilateral technique from 2004 to 2017. Traditional technique patients had a breast revision rate of 64.1% and a nipple revision rate of 42.3% after secondary nipple reconstruction. Destination Design patients had a breast revision rate of 44.3% (P = 0.0394) and a nipple revision rate of 37.9% (P = 0.689) after primary nipple reconstruction. The BREAST-Q questionnaire was sent to nine traditional technique patients with 8 responses (89%), and 35 Destination Design patients with 25 responses (71%). Survey results showed that traditional technique and Destination Design patients had an overall breast satisfaction rate of 67.5% and 63.9%, respectively. Conclusions: The Destination Design msTRAM breast reconstruction technique leads to a statistically significant reduction in breast flap revisions, and allows for equally accurate immediate nipple reconstruction compared with traditional methods with no additional complications. Overall patient satisfaction is comparable with both techniques.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".