Direct-to-implant Breast Reconstruction with Autoderm
Bibliographic record
Abstract
Background: Traditional transverse mastectomies yield suboptimal results in women with higher body mass index, wide breast footprint, and ptotic breasts. An option for this patient population is a reduction-pattern style mastectomy, and recruiting an inferiorly based dermal flap using the lower mastectomy flap. This is analogous to a vascularized dermal matrix supporting the lower pole of the implant, termed “Autoderm” breast reconstruction. This allows for aesthetically appealing skin reduction mastectomies with the added safety of a vascularized dermal flap to facilitate an immediate direct-to-implant breast reconstruction. This study assesses patient satisfaction using the validated BRECON-31 questionnaire to enhance shared-decision making with women contemplating breast reconstruction. Methods: A 2-year retrospective review of women who underwent Autoderm direct-to-implant breast reconstruction comparing patients who underwent unilateral and bilateral reconstruction in terms of characteristics, complications, and BRECON-31 scoring. Results: Overall patient scores were high (81.6 of 100). In particular, women scored very high on self-image (85.0), arm concerns (86.4), intimacy (87.4), satisfaction (88.3), and expectations subscales (85.5). Women choosing bilateral reconstruction outperformed unilateral reconstruction in every subgroup, but only attained statistical significance in the “self-consciousness” subgroup. Compared with a historical cohort of a mix of implant reconstruction types, Autoderm patients showed improved satisfaction (88.3 versus 82.5;P= 0.07) and breast appearance (73.9 versus 66.8;P= 0.06), approaching significance. Safety was demonstrated by low major complications (4.7%) and low implant loss rates (2.3%). Conclusions: Autoderm breast reconstruction is a safe option in women with large, ptotic breasts, with patients reporting high satisfaction using a validated instrument.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".