Wide variation in surgical techniques to repair incisional hernias: a survey of practice patterns among general surgeons
Bibliographic record
Abstract
BACKGROUND: The purpose of this research was to examine the self-reported practice patterns of Canadian general surgeons regarding the elective repair of incisional hernias. METHODS: A mail survey was sent to all general surgeons in Canada. Data were collected regarding surgeon training, years in practice, practice setting and management of incisional hernias. Surgeons were asked to describe their usual surgical approach for a patient with a midline incisional hernia and a 10 × 6 cm fascial defect. RESULTS: Of the 1876 surveys mailed out 555 (30%) were returned and 483 surgeons indicated that they perform incisional hernia repair. The majority (62%) have been in practice > 10 years and 73% regularly repair incisional hernias. In response to the clinical scenario of a patient with an incisional hernia, 74% indicated that they would perform an open repair and 18% would perform a laparoscopic repair. Ninety eight percent of surgeons would use mesh, 73% would perform primary fascial closure and 47% would perform a component separation. The most common locations for mesh placement were intraperitoneal (46%) and retrorectus/preperitoneal (48%). The most common repair, which was reported by 37% of surgeons, was an open operation, with mesh, with primary fascial closure and a component separation. CONCLUSIONS: While almost all surgeons who perform incisional hernia repairs would use permanent mesh, there was substantial variation reported in surgical approach, mesh location, fascial closure and use of component separation techniques. It is unclear how this variability may impact healthcare resources and patient outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".