RETRACTED: Influence of urinary tract infections on the incidence of surgical site infections following hip fracture surgery: A systematic review and 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
Abstract The prevalence of surgical site infections (SSIs) following hip fracture surgery poses a substantial challenge, compounding patient morbidity and healthcare costs. This systematic review and meta‐analysis investigate the potential correlation between perioperative urinary tract infections (UTIs) and the subsequent risk of SSIs, aiming to illuminate the impact of UTIs on postoperative outcomes in this vulnerable population. We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines, utilising the PICO framework to define our search strategy across PubMed, Embase, Web of Science and the Cochrane Library. Our inclusion criteria encompassed randomised controlled trials, cohort studies and case–control studies that reported on SSIs following hip fracture surgery in patients with UTIs. Quality was assessed using the Newcastle‐Ottawa Scale, and heterogeneity was quantified using the I 2 statistic. A random‐effects model was applied due to significant heterogeneity, and a sensitivity analysis assessed the stability of the results. Six studies met the inclusion criteria, demonstrating high methodological quality. The analysis included studies from 2016 to 2021, with sample sizes ranging from 402 to 31 621 participants. A significant association was found between UTIs and SSIs, with an odds ratio of 2.79 (95% CI: 1.72–4.54, p < 0.001). Sensitivity analysis confirmed the robustness of the results, and no publication bias was detected. Perioperative UTIs significantly increase the risk of SSIs in patients undergoing hip fracture surgery. Proactive treatment of UTIs may be crucial for reducing the incidence of SSIs and improving surgical outcomes in this demographic.
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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 | 0.027 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.044 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".