The Best of Abdominal Wall Reconstruction
Bibliographic record
Abstract
LEARNING OBJECTIVES: After reviewing this article, the participant should be able to: 1. List major risk factors for hernia formation and for failure of primary repair. 2. Outline an algorithmic approach to anterior abdominal wall reconstruction based on the degree of contamination, components involved in the deficit, and width of the hernia defect. 3. Describe appropriate indications for synthetic and biological mesh products. 4. List common flaps used in anterior abdominal wall reconstruction, including functional restoration strategies. 5. Describe the current state of the art of vascularized composite tissue allotransplantation strategies for abdominal wall reconstruction. SUMMARY: Plastic surgeons have an increasingly important role in abdominal wall reconstruction-from recalcitrant, large incisional hernias to complete loss of abdominal wall domain. A review of current algorithms is warranted to match evolving surgical techniques and a growing number of available implant materials. The purpose of this article is to provide an updated review of treatment strategies to provide an approach to the full spectrum of abdominal wall deficits encountered in the modern plastic surgery practice.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".