Risk factors for subsequent vertebral fractures after percutaneous vertebral augmentation in Asian populations: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To identify the risk factors for subsequent vertebral fractures after percutaneous vertebral augmentation through the meta-analysis. METHODS: Articles from 2019 to 2024 were retrieved from PubMed, Cochrane Library, Embase, and Web of Science. The quality of included studies was assessed using the Newcastle-Ottawa Scale (NOS), while data analysis was performed with R (The R Project for Statistical Computing). RESULTS: Fourteen articles comprising data from 5,673 patients were included in the analysis. Statistically significant differences were identified for age, gender, T-score (measured by dual-energy X-ray absorptiometry), body mass index (BMI), Computed tomography Hounsfield unit (CT HU) value, intravertebral cleft (IVC), multi-segment vertebral fractures, and bone cement leakage. In contrast, no statistically significant differences were observed for hypertension history, diabetes history, thoracolumbar vertebral fracture, postoperative Cobb angle, surgical method（percutaneous vertebroplasty/percutaneous kyphoplasty）, puncture method (unilateral/bilateral puncture), or bone cement volume. CONCLUSION: In Asian populations, advanced age, female, low T-score, low BMI, low CT HU values, presence of IVC, multi-segment vertebral fractures, and bone cement leakage are identified as significant risk factors for subsequent vertebral fractures following PVA. Conversely, a history of anti-osteoporosis treatment is identified as a protective factor, whereas hypertension history, diabetes history, thoracolumbar vertebral fracture, postoperative Cobb angle, surgical method, puncture method, and bone cement volume demonstrate no significant correlation with subsequent vertebral fractures after PVA.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".