Practice patterns and predictors of prophylactic endoscopic clip usage during polypectomy
Bibliographic record
Abstract
Abstract Background and study aims Prophylactic endoscopic clips are commonly placed during polypectomy to reduce risk of delayed bleeding, although evidence to support this practice is unclear. Our study aimed to: (1) identify variables associated with prophylactic clip use; (2) explore variability between endoscopists’ clipping practices and (3) study temporal trends in prophylactic clip use. Patients and methods This was a retrospective cohort study in a high-volume unit dedicated to screening-related colonoscopies. Colonoscopies involving polypectomy from 2008 to 2014 were reviewed. The primary outcome was prophylactic clipping status, both at the patient level and per polyp. Hierarchical regression models yielded adjusted odds ratios (AORs) to determine predictors of prophylactic clipping. Results A total of 8,366 colonoscopies involving 19,129 polypectomies were included. Polyp size ≥ 20 mm was associated with higher clip usage (AOR 2.94; 95 % CI: 2.43, 3.54) compared to polyps < 10 mm. Right-sided polyps were more likely to be clipped (AOR 2.78; 95 % CI: 2.34, 3.30) relative to the rectum. Surgeons clipped less than gastroenterologists (OR 0.52; 95 % CI: 0.44, 0.63). From 2008 to 2014, the crude proportion of prophylactically clipped cases increased by 7.4 % (95 % CI: 7.1, 7.6) from 1.9 % to 9.3 %. Significant inter-endoscopist variability in clipping practices was observed, notably, for polyps < 10 mm. Conclusions Prophylactic clip usage was correlated with established risk factors for delayed bleeding. Significantly increased clip usage over time was shown. Given that evidence does not clearly support prophylactic clipping, there is a need to educate practitioners and limit healthcare resource utilization.
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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 | 0.007 |
| 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) | 0.001 | 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".