RETRACTED: Comparison of the effects of laparoscopic and open hysterectomy on surgical site wound infections in patients with endometrial cancer: A meta‐analysis
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
This meta-analysis aimed to compare laparoscopic hysterectomy (LH) and open hysterectomy (OH) in terms of surgical site wound infection, length of hospital stay, and postoperative complications in patients with endometrial cancer (EC). PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, VIP, and Wanfang databases were comprehensively searched for studies on OH and LH for EC published between 2008 and July 2023, in any language. The literature was screened according to the inclusion and exclusion criteria, and the quality of the included case-control studies was assessed using the Newcastle-Ottawa Scale. Data were collated and analysed using Stata 17.0 software. A total of 1245 articles were screened according to the search strategy, and ultimately 15 studies were included in this meta-analysis, with a total of 1606 patients with EC, of which 751 were treated with LH and 855 with OH. The results showed that the rate of postoperative wound infection was significantly higher (OR: 0.290; 95% CI: 0.169-0.496, p < 0.001), the length of hospital stay was significantly longer (SMD: -1.976, 95% CI: -2.669 to -1.283, p < 0.001), and the incidence of postoperative complications was significantly higher (OR: 0.366; 95% CI: 0.280-0.478, p < 0.001) in the OH group than in the LH group. This study showed that LH was superior to OH for the treatment of EC and is associated with a lower rate of wound infection, shorter length of hospitalisation, and a reduced risk of complications. Thus, our findings support the choice of LH over OH for EC.
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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 | 0.023 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.059 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".