Prophylactic Operative Interventions for Preventing Parastomal Hernias after Colorectal Surgery
Bibliographic record
Abstract
The most common long-term stoma-related morbidity following colorectal surgery is parastomal hernia formation. Given the risk of developing parastomal hernias and the risk of postoperative complications following their repair, practices have evolved to incorporate prophylactic strategies to reduce the risk of parastomal hernia formation after colorectal surgery. The majority of the data forming the evidence base for parastomal hernia prophylaxis pertains to patients undergoing end colostomy formation in the setting of colorectal cancer. The only prophylactic intervention for prevention of parastomal hernia formation with substantial amounts of high-quality data is the insertion of prophylactic mesh at the index operation for patients undergoing formation of a permanent end colostomy. Other interventions that have been proposed but have less published data substantiating their use include lateral pararectus stoma placement, extraperitoneal stoma creation, circular stoma trephine, and small fascial defects. This chapter will review each of these interventions in detail, along with the associated literature supporting or refuting their use. Additionally, we will discuss other important issues regarding the evidence base for parastomal hernia prophylaxis, parastomal hernia classifications, and risk factors for developing parastomal hernias.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".