Do North American colorectal surgeons use mesh to prevent parastomal hernia? A survey of current attitudes and practice
Bibliographic record
Abstract
Background: The use of prophylactic mesh in end colostomy procedures has been shown to reduce the rate of parastomal hernia. However, the degree to which the practice has been adopted clinically remains unknown. We conducted a study to evaluate the current opinions and practice patterns of Canadian and US colorectal surgeons with regard to the use of prophylactic mesh in end colostomy. Methods: Between May and July 2017, we conducted an internet-based survey of colorectal surgeons in Canada and the United States (selected at random). Using a questionnaire designed and tested for this study, we assessed the rate of mesh use, types of mesh and placement techniques, and perceived barriers and facilitators associated with the practice. Results: Forty-eight (51.6%) of 93 invited Canadian surgeons and 253 (16.6%) of 1521 invited US surgeons responded (overall response rate 18.6%). Of the 301 respondents, 32 (10.6%) were currently using mesh, 32 (10.6%) had previously used mesh, and 237 (78.7%) had never used mesh. Of 29 respondents currently using mesh, 12 (41.4%) used it only in selected patients; the majority used a sublay technique (20 [69.0%]) and biologic mesh (17 [58.6%]). Most respondents agreed that parastomal hernias are common and negatively affect quality of life; however, there remained concerns about evidence quality and the perceived risk associated with mesh among those who had never or had previously used mesh. Conclusion: Prophylactic mesh placement remains relatively uncommon; when used, biologic mesh was the most common type. Many surgeons were not convinced of the safety or efficacy of prophylactic mesh placement.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".