The Utility of Lighted Ureteral Stents in Laparoscopic Colorectal Resection: A Survey of Canadian Surgeons
Bibliographic record
Abstract
BACKGROUND: Establishing the exact location of the ureters is critical in preventing ureteric injury during colorectal surgery. In laparoscopic colorectal resections this identification can be facilitated by the pre-operative insertion of lighted ureteral stents (LUS). LUS may also serve as an invaluable educational aid during the teaching of colorectal surgery. However, the available evidence does not support the routine use of stents as an injury prevention measure. Furthermore, stent insertion carries inherent risks of ureteric injury. The objective of this study was to determine the frequency of use and indications for LUS in laparoscopic colorectal resections among Canadian surgeons. METHODS: A seven-question survey was administered to Canadian surgeons through the monthly Canadian Association of General Surgeons (CAGS) e-news over a period of three months. The questions focused on surgeon demographics, experience with laparoscopic colon resections and the use of stents. RESULTS: Seventy-five surgeons completed the survey. There was a wide range of experience among the surgeons in terms of years in practice. The majority (84%) reported performing laparoscopic colorectal resections and of those 65% reported performing less than 25 resections a year. Only 26% of surgeons used LUS during laparoscopic resections. Furthermore, 75% of LUS users did not have sub-specialty training, 69% performed less than 25 resections per year and 50% were in practice for less than five years. When used, LUS were inserted for diverticular disease (100%), left colon resection (88%) and low anterior resections (75%). CONCLUSION: The majority of surgeons across Canada do not use LUS for laparoscopic colorectal resections. Of those performing laparoscopic colorectal resections, there may be a preference to use LUS for complex cases and by novice operators. This data suggests that proponents of LUS deem that it may have a role in diverticular disease.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".