Wide variation in surgical techniques to repair incisional hernias: a survey of practice patterns among general surgeons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it