RETRACTED: Effects of laparoscopic splenectomy on surgical site wound infection in patients with spleen rupture: 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
A meta-analysis was performed to compare the effects of laparoscopic splenectomy (LS) and open splenectomy (OS) for splenic rupture on postoperative surgical site wound infections and postoperative complications. A comprehensive computerised search was conducted for studies comparing LS with OS for the treatment of splenic rupture in the PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP, and Wanfang databases, with the search including studies published in any language between the creation of the databases and August 2023. Two researchers independently screened the literature and extracted the data. Literature quality was assessed using the Newcastle-Ottawa Scale, and the included data were collated and analysed using Stata 17.0 software for meta-analysis. Twenty-two studies involving 1545 patients were included. LS was superior to OS in the following aspects: reduced risk of postoperative surgical site wound infection (OR = 0.19, 95% CI: 0.11-0.34, p = 0.000), shortened hospital stay (standardised mean difference = -1.73, 95% CI: -2.05 to -1.40, p = 0.000), and reduced postoperative complication rate (OR = 0.22, 95% CI: 0.16-0.31, p = 0.000). Compared with OS, LS has a lower rate of postoperative wound infection, shorter hospital stay, and reduced rate of postoperative complications. LS is safe and effective for the treatment of splenic rupture and can be promoted clinically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.059 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".