An Algorithmic Approach to Umbilical Inset during DIEP Flap Reconstruction
Bibliographic record
Abstract
SUMMARY: An aesthetically pleasing umbilicus is a critical component to the overall cosmesis and resultant patient satisfaction after deep inferior epigastric artery perforator (DIEP) flap breast reconstruction. Because of variables in body habitus, comorbidities, and technical aspects of the procedure, patients undergoing DIEP flap breast reconstruction are at a higher risk of umbilical complications and poor aesthetic appearance of the neoumbilicus compared with those undergoing cosmetic abdominoplasty. To minimize these potential problems and maximize the overall aesthetic appearance of the abdomen, the authors propose an algorithmic approach to umbilical inset after DIEP flap harvest that takes into account several critical factors: the thickness of the subcutaneous tissue of the abdominal flap, the length of the umbilical stalk, and the depth of the umbilical bowl. This simple algorithmic approach is a useful tool that will assist surgeons in minimizing umbilical complications and delivering a superior cosmetic appearance to the abdominal donor site in DIEP flap reconstruction.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".