Abdominal Wall Reconstruction Following Strangulated Recurrent Incisional Hernia During Pregnancy
Bibliographic record
Abstract
By the 10-year follow-up, incisional hernias appear at a rate of 18.7%, but it is still unknown how often this complication occurs during pregnancy. It is challenging to diagnose a hernia in a pregnant patient, and there is a lack of literature reporting on these cases. We present the case of a 26-week gestation, morbidly obese patient who presented with a strangulated, recurrent incisional hernia. She required bowel resection and multiple subsequent incisional hernia repairs. Finally, a bioprosthetic reinforced component separation repair was used on the fifth hernia occurrence. The patient remains free of recurrent hernia but still experiences pain in her abdomen and back. An ideal technique for repairing incisional hernias in pregnant patients has yet to be established, but it is clear that each case must be dealt with on an individual basis. It is essential for surgeons to consider the length of the surgery, the presence of contamination, the age of the fetus, the size of the defect, and the chances of recurrence before selecting which technique to employ when repairing abdominal wall hernias in pregnancy. J Med Cases • 2013;4(12):796-798 doi: http://dx.doi.org/10.4021/ jmc1504w
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".