Risk Factors for the Development of Olecranon Bursitis—A Large-Scale Population-Based Study
Bibliographic record
Abstract
Background: Olecranon bursitis (OB) involves fluid accumulation in the bursa, with common causes being trauma and preexisting conditions. Its incidence is difficult to quantify, and risk factors such as diabetes, obesity, and male gender are frequently noted. Hyperlipidemia has been linked to musculoskeletal disorders, but its role as a risk factor for OB remains unexplored. This study aimed to investigate the association between OB and hyperlipidemia, diabetes, obesity, cardiovascular disease, and statin use. Methods: A retrospective cohort study analyzed a large-scale database (2005–2020), ultimately including 10,301 patients with olecranon bursitis and 44,608 controls after applying exclusion criteria. Participants were aged 18–90 years, with BMI between 10 and 55. Key variables such as smoking, diabetes, hyperlipidemia, statin use, cardiovascular diseases (CVDs), and cerebrovascular accidents (CVAs) were analyzed. Logistic regression models were applied with stabilized inverse probability of treatment weighting (IPTW) to estimate odds ratios (ORs) for risk factors, and p-values were adjusted using the Benjamini–Hochberg method. Results: OB was significantly associated with male gender (OR: 1.406; p < 0.0001), hyperlipidemia (OR: 1.239; p < 0.0001), statin use (OR: 1.117; p = 0.0035), and smoking (OR: 1.068; p = 0.0094). Age and BMI were significant continuous variables influencing OB risk, particularly in older patients and those with elevated BMI. CVDs and diabetes were not significantly linked to OB. Hyperlipidemia increased OB risk, especially in males and individuals with higher BMI. Conclusions: Male gender, hyperlipidemia, and smoking are key risk factors for OB, with hyperlipidemia posing a notable risk in older individuals and those with higher BMI. Statin use did not significantly alter OB risk in hyperlipidemic patients. Further studies are needed to clarify the mechanisms behind these associations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".