Incidence, risk factors and prevention of stoma site incisional hernias: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: Stoma reversal might lead to a stoma site incisional hernia. Recently, prophylactic mesh reinforcement of the stoma site has gained increased attention, supporting the need for accurate data on the incidence of and risk factors for stoma site incisional hernia and to identify high-risk patients. The aim of this study was to assess incidence, risk factors and prevention of stoma site incisional hernias. METHOD: Embase, MEDLINE, Web of Science, Cochrane and Google Scholar databases were searched. Studies reporting the incidence of stoma site incisional hernia after stoma reversal were included. Study quality was assessed with the Newcastle-Ottawa Scale and Cochrane risk of bias tool. Data on incidence, risk factors and prophylactic mesh reinforcement were extracted. RESULTS: Of 1440 articles found, 33 studies comprising 4679 reversals were included. The overall incidence of incisional hernia was 6.5% [range 0%-38%, median follow-up 27.5 (17.54-36) months]. Eleven studies assessed stoma site incisional hernia as the primary end-point, showing an incidence of 17.7% [range 1.7%-36.1%, median follow-up 28 (15.25-51.70) months]. Body mass index, diabetes and surgery for malignant disease were found to be independent risk factors, as derived from eight studies. Two retrospective comparative cohort studies showed significantly lower rates of stoma site incisional hernia with prophylactic mesh reinforcement compared with nonmesh controls [6.4% vs 36.1% (P = 0.001); 3% vs 19% (P = 0.04)]. CONCLUSION: Stoma site incisional hernia should not be underestimated as a long-term problem. Body mass index, diabetes and malignancy seem to be potential risk factors. Currently, limited data are available on the outcomes of prophylactic mesh reinforcement to prevent stoma site incisional hernia.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".