RETRACTED: Impact of type 2 diabetes on surgical site infections and prognosis post orthopaedic surgery: A systematic review and <scp>meta‐analysis</scp>
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
BACKGROUND: The escalating prevalence of type 2 diabetes raises concerns about adverse postoperative outcomes like surgical site infections (SSIs) and deep vein thrombosis (DVT) in orthopaedic surgeries. This meta-analysis aims to resolve inconclusive evidence by systematically quantifying the risks in type 2 diabetic patients compared to non-diabetic individuals. METHODS: The meta-analysis was conducted adhering to the PRISMA guidelines and based on the PICO framework. Four primary databases were searched: PubMed, Embase, Web of Science and the Cochrane Library, with no temporal restrictions. Studies included were either prospective or retrospective cohort studies published in English or Chinese, which assessed orthopaedic surgical outcomes among adult type 2 diabetic and non-diabetic patients. The meta-analysis employed the Newcastle-Ottawa Scale for quality assessment and used both fixed-effect and random-effects models for statistical analysis based on the level of heterogeneity. RESULTS: Out of 951 identified articles, nine studies met the inclusion criteria. The odds ratio (OR) for developing postoperative SSIs among diabetic patients was 1.63 (95% CI: 1.19-2.22), indicating a significantly elevated risk compared to non-diabetic subjects. Conversely, no statistically significant difference in the risk of postoperative DVT was found between the two groups (OR: 0.82; 95% CI: 0.55-1.22). Sensitivity analysis confirmed the stability of these outcomes. CONCLUSIONS: Patients with type 2 diabetes are at a higher risk of developing SSIs post orthopaedic surgery compared to non-diabetic individuals. However, both groups demonstrated comparable risks for developing postoperative DVT.
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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 | 0.028 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".