A Brief History of Breast Reconstruction and a Discussion of Two Common Autologous Breast Reconstruction Surgeries: The Free Deep Inferior Epigastric Perforator Flap and the Free Transverse Rectus Abdominis Musculocutaneous Flap
Bibliographic record
Abstract
Breast cancer has long been recognized as a challenging disease to treat. In the past 100 years the five-year survivalof the disease has increased from a dismal 4-30% up to 87%.1,2 Surgical techniques have advanced from barbaricremoval of the breast in the 15th and 16th century to advanced, highly technical tumour excisions with breastreconstructioninvolving artificial implants or tissues from the patient’s own body. The Deep Inferior EpigastricPerforator Flap (DIEP) and the Transverse Rectus Abdominis Musculocutaneous (TRAM) Flap are two commonlyused autologous free flap techniques. Evolution of technique has led to both procedures having high success ratesand low complication rates. Whether a DIEP is performed over a TRAM typically depends on surgeon experienceand patient anatomy. Selection of which flap to use and whether there are any clear advantages of DIEP over freeTRAM is an ongoing debate. Common complications for the free TRAM flap are mainly at the abdominal donor sitewhile the DIEP complications are in the flap itself. Current studies suggest that the three most important factors inselecting a flap are patient’s obesity, patient’s arterial anatomy at the donor site, and whether a bilateral flap is beingperformed. Ideally, a multi-center study would be performed which examines a wide range of donor site and flapcomplications to determine whether the increased time and risk of performing the DIEP equates to better patientoutcomes compared to the free TRAM. For now, most authors advocate for proper patient selection, along withintra-operative assessment of the patient’s perforating vessels as the best way to optimize outcomes and avoidcomplications.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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".