Association between malnutrition status and total joint arthroplasty periprosthetic joint infection and surgical site infection: a systematic review meta-analysis
Bibliographic record
Abstract
BACKGROUND: Malnutrition is a state resulting from lack of intake or uptake of nutrition. Investigating the association between malnutrition and postoperative complications is essential for enhancing patient outcomes in total joint arthroplasty (TJA). This meta-analysis aimed to investigate the impact of malnutrition on the incidence of surgical site infections (SSIs) and periprosthetic joint infections (PJIs) following TJA. METHODS: The data were searched from databases including PubMed, Embase, Web of Science, and Cochrane Library inception through July 19 2023, without time restrictions. Inclusion criteria focused on studies examining malnutrition as a risk factor for SSIs and PJIs postarthroplasty, providing sufficient data for calculating odds ratios (ORs) and 95% confidence intervals (CIs). Methodological quality was assessed using the Newcastle‒Ottawa Scale, and statistical analyses were executed in Stata version 17. RESULTS: A total of 1,025 articles were screened, and 9 studies satisfying the predefined inclusion criteria were consequently selected for this meta-analysis. Studies indicated that malnutrition is significant factor to the heightened incidence of both SSIs and PJIs following TJA procedures. Our pooled results yielded aggregated ORs of 2.60 for SSIs and 3.44 for PJIs, with respective 95% CIs of 2.10-3.10 and 2.35-4.53. The heterogeneity of malnutrition as a risk factor for postoperative SSI was I2 = 0.0% (p = 0.592), and for PJI was I2 = 0.0% (p = 0.422). Egger's linear regression test showed no significant publication bias (p > 0.05). CONCLUSIONS: Malnutrition is a significant risk factor for SSIs and potentially PJIs in patients undergoing TJA. Preoperative optimization strategies targeted at malnourished patients are suggested to minimize postoperative complications clinically.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.054 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".