A116 PROPHYLACTIC CLIPPING TO PREVENT DELAYED COLONIC POST-POLYPECTOMY BLEEDING: META-ANALYSIS OF RANDOMIZED AND OBSERVATIONAL STUDIES
Bibliographic record
Abstract
Abstract Background Delayed post-polypectomy bleeding (DPPB) is a commonly described adverse event following polypectomy. Prophylactic clipping may prevent DPPB in some patient subgroups. We performed a meta-analysis to assess both the efficacy and real-world effectiveness of prophylactic clipping. Aims We performed a meta-analysis to assess both the efficacy and real-world effectiveness of prophylactic clipping. Methods We performed a database search through March 2020 for clinical trials or observational studies assessing prophylactic clipping and DPPB. Pooled risk ratios (RR) were calculated using random effects models. Subgroup, sensitivity and meta-regression analyses were performed to elucidate clinical or methodological factors associated with effects on outcomes. Results A total of 2,771 citations were screened, with 11 randomized controlled trials (RCTs) and 9 observational studies included, representing 24,670 colonoscopies. DPPB occurred in 2.0% of patients overall. The pooled RR of DPPB was 0.47 (95% CI 0.29 – 0.77) from RCTs enrolling only patients with polyps ≥ 20 mm. Remaining pooled RCT data did not demonstrate a benefit for clipping. The pooled RR of DPPB was 0.96 (95% CI 0.61 – 1.51) from observational studies including all polyp sizes. For patients with proximal polyps of any size, the RR was 0.73 (95% CI 0.33 - 1.62) from RCTs. Meta-regression confirmed that polyp size ≥ 20 mm significantly influenced the effect of clipping on DPPB. Conclusions Pooled evidence demonstrates a benefit when clipping polyps measuring ≥ 20 mm, especially in the proximal colon. In lower-risk subgroups, prophylactic clipping should not be performed. Funding Agencies None
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.055 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".