Immediate DIEP Breast Reconstruction in a Trans Woman with Breast Cancer
Bibliographic record
Abstract
Breast cancer is a leading cause of cancer among cisgender women worldwide, with an incidence that continues to rise. Breast reconstruction is increasingly recognized as an integral part of breast cancer management. In tandem, the population of trans women is also increasing. However, few studies have focused on the management of breast cancer in this population, given that estrogen therapy has the potential to increase the risk of breast cancer. We present the case of a 51-year-old trans woman diagnosed with right breast cancer following a 32-year history of estrogen therapy. She presented to our clinic for consultation and immediate breast reconstruction. She opted for bilateral skin-sparing mastectomies, sentinel lymph node biopsies, and immediate breast reconstruction using deep inferior epigastric perforator free flaps. There were no postoperative flap complications. Final pathology revealed invasive mucinous carcinoma, devoid of lymph node involvement. The patient was satisfied with her reconstruction and is currently planning for nipple reconstruction. To the authors' knowledge, this is the first reported case in the literature of bilateral deep inferior epigastric perforator free flap reconstruction in the trans female population after breast cancer diagnosis. This case underscores the need for a holistic treatment approach and heightened vigilance among trans women on estrogen therapy. It also highlights that breast reconstruction should be considered and offered to this patient population.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".