Prophylactic Endoscopic Clipping Does Not Prevent Delayed Postpolypectomy Bleeding in Routine Clinical Practice: A Propensity Score–Matched Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Delayed postpolypectomy bleeding (DPPB) is a relatively common adverse event. Evidence is conflicting on the efficacy of prophylactic clipping to prevent DPPB, and real-world effectiveness data are lacking. We aimed to determine the effectiveness of prophylactic clipping in preventing DPPB in a large screening-related cohort. METHODS: We manually reviewed records of patients who underwent polypectomy from 2008 to 2014 at a screening facility. Endoscopist-, patient- and polyp-related data were collected. The primary outcome was DPPB within 30 days. All unplanned healthcare visits were reviewed; DPPB cases were adjudicated by committee using a criterion-based lexicon. Multivariable logistic regression was performed, yielding adjusted odds ratios (AORs) for the association between clipping and DPPB. Secondary analyses were performed on procedures where one polyp was removed, in addition to propensity score-matched and subgroup analyses. RESULTS: In total, 8,366 colonoscopies involving polypectomy were analyzed, yielding 95 DPPB events. Prophylactic clipping was not associated with reduced DPPB (AOR 1.27; 0.83-1.96). These findings were similar in the single-polyp cohort (n = 3,369, AOR 1.07; 0.50-2.31). In patients with one proximal polyp ≥20 mm removed, there was a nonsignificant AOR with clipping of 0.55 (0.10-2.66). Clipping was not associated with a protective benefit in the propensity score-matched or other subgroup analyses. DISCUSSION: In this large cohort study, prophylactic clipping was not associated with lower DPPB rates. Endoscopists should not routinely use prophylactic clipping in most patients. Additional effectiveness and cost-effectiveness studies are required in patients with proximal lesions ≥20 mm, in whom there may be a role for prophylactic clipping.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".