Risk factors for osteomyelitis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Osteomyelitis (OM) is a serious infection of the bone, often resulting from diabetic foot ulcers, trauma, or surgical interventions. Given its complex pathophysiology and recurrent nature, identifying reliable risk factors is essential for early diagnosis and intervention. This study identifies independent OM risk factors through a systematic review and meta-analysis method. METHODS: A search was conducted across several databases up to January 2025. Data were extracted independently, and study quality was assessed using the Newcastle-Ottawa Scale. Statistical analysis was performed with RevMan 5.3 and Stata 15.0 software, using univariate and multivariate analyses. Heterogeneity was assessed by methods of subgroup analysis and sensitive analysis, and publication bias was evaluated with Egger's test and funnel plots. RESULTS: A total of 4019 potential articles were systematically reviewed, with 27 studies (n = 11 941 participants) were included. These studies were analyzed to identify risk factors across five categories: demographic features, medical history, clinical features, laboratory findings, and bacterial characteristics. Medical history factors, such as history of foot ulcers/foot disease (OR = 2.520), operation duration time (>3 hours, OR = 1.740), and incision length >10 cm (OR = 6.530), were significant. Clinical features including inflamed ulcer (OR = 6.200), fever (OR = 2.200), and ulcer size >4/5 cm 2 (OR = 2.740) also increased OM risk. Laboratory findings such as elevated HbA1c (OR = 1.140), hypoalbuminemia (OR = 1.740), anemia (OR = 1.540), and microbiological perspective such as polymicrobial infections (OR = 2.580) were also recognized as independent risk factors of OM. Subgroup analysis accounted for heterogeneity arising from the type of OM and different risk factor characteristics. Sensitivity analysis confirmed the reliability of the results. Additionally, both funnel plots and Egger tests showed no evidence of publication bias. CONCLUSIONS: This is the first meta-analysis to integrate univariate and multivariate evidence on OM risk factors. It establishes clinically preventable risk factors across OM subtypes, enabling early targeted interventions to reduce amputations and healthcare burdens.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".