Truncal‐based perforator flaps for autologous breast reconstruction: A review of 975 flaps and their clinical application
Bibliographic record
Abstract
BACKGROUND: When autologous breast reconstruction is desired and deep inferior epigastric artery perforator (DIEP) flap is inadequate or unavailable, other perforator flaps in the truncal region should not be disregarded. This study aimed to review all truncal-based perforator flaps used for autologous breast reconstruction to identify clinical indications and outcomes of alternate perforator flaps. METHODS: From 2013 to 2018, patients undergoing truncal-based perforator flap breast reconstruction were reviewed and data recorded for; indications, pre-operative and intra-operative treatment decisions, flap failures, take-backs, and revisions. Compared to the gold standard of the DIEP flap, alternate truncal-based flaps were evaluated for their reconstructive merit and application. RESULTS: A total of 975 perforator flaps were harvested circumferentially around the lower trunk. As an alternative or adjunct to the DIEP flap (n = 633, 65%), perforator flaps were harvested based on the superficial inferior epigastric, the deep and superficial circumflex iliac arteries, the intercostal, and lumbar arteries (n = 342, 35%). Overlapping vascular territories facilitate the safe harvest of these alternate flaps with 0.8% of flaps requiring take back (n = 8) and 0.2% flap failure rate (n = 2). There was no difference in peri-operative outcomes between anterior abdominal and alternate truncal-based flaps (p > .05). CONCLUSIONS: Circumferential harvest of alternate truncal flaps is an appropriate option for autologous reconstruction with comparable peri-operative and long-term outcomes as compared to flaps from the anterior abdomen.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".