It all doesn’t always have to go: abdominal wall reconstruction involving selective synthetic mesh explantation with biologic mesh salvage
Bibliographic record
Abstract
The comparative performance of synthetic and biologic meshes in complex and contaminated abdominal wall repairs remains controversial. Though biologic meshes are generally favoured in contaminated fields, this practice is based on limited data. Standard dictum regarding infected mesh is to either explant it early or pursue aggressive conservation measures depending on mesh position and composition. Explantation is typically morbid, leaving the patient with recurrent hernias and few reconstructive options. We report a case in which a hernia repaired with synthetic mesh recurred and was reconstructed with underlay biologic mesh. Delayed wound hematoma occurred after initiating anticoagulation for late postoperative pulmonary embolism, which became chronically infected. After multiple failed attempts at medical and interventional salvage of the mesh infection, the patient underwent selective explantation of synthetic mesh with conservation of the underlying biological mesh. She recovered completely without recurrent abdominal wall failure at long-term follow-up. We suggest the "salvageable" characteristics of biologic meshes may allow conservation, rather than explantation, in select cases of infection.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".