RETRACTED: Wound infection prevention strategies in colorectal endoscopic mucosal resection: A meta‐analysis of prophylactic measures
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Colorectal endoscopic mucosal resection (EMR) is associated with the risk of postoperative wound infections, prompting investigations into effective prophylactic measures. This meta-analysis aimed to evaluate the efficacy of various prophylactic interventions in reducing the incidence of wound infections following EMR. Adhering to PRISMA guidelines, we conducted a comprehensive search across multiple databases for randomized controlled trials (RCTs) and cohort studies from 2015 to 2022. We included studies that compared the efficacy of antibiotic prophylaxis and antiseptic measures, with clear data on post-procedure infection rates. Eight studies met our inclusion criteria, and data were extracted for meta-analysis. The risk of bias was assessed using the Cochrane Collaboration tool and the Newcastle-Ottawa Scale. The meta-analysis included 3765 patients from eight RCTs. Prophylactic antibiotics (cefixime and cefuroxime) showed moderate to high efficacy, with infection rates as low as 0% and 0.76%. Prophylactic endoscopic closure and clipping showed the highest efficacy, with zero reported infections. The standardized surgical site infection prevention bundle had lower effectiveness, with an infection incidence of 3.83%. The risk of bias assessment indicated potential performance bias due to lack of blinding, but overall evidence quality was upheld by proper random sequence generation and diligent outcome data monitoring. The effectiveness of specific prophylactic measures, notably prophylactic antibiotics and mechanical closure techniques, has been shown in significantly reducing the risk of wound infections following colorectal EMR.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.045 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".