Risk factors for new vertebral compression fractures after vertebroplasty: a meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The risk factors for new vertebral compression fractures (VCFs) after vertebroplasty are unclear. The aim of this meta-analysis was to identify potential risk factors. METHODS: A systematic electronic literature search was performed using the following databases: PubMed, Embase and Cochrane Library; the databases were searched from the earliest available records in 1966 to May 2015. Pooled odds ratios (ORs) or standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using random- or fixed-effects models. The Newcastle-Ottawa scale was used to evaluate the methodological quality of the studies, and Stata 11.0 was used to analyse the data. RESULTS: The primary factors that were associated with new fractures after vertebroplasty were low bone mineral density (SMD -0.375; 95% CI -0.579 to -0.171), steroid usage (OR 2.632; 95% CI 1.399 to 4.950) and the presence of multiple treated vertebrae (OR 2.027; 95% CI 1.442 to 2.851). The data did not support that age, sex, body mass index, non-steroidal anti-inflammatory drug usage, vacuum cleft, thoracolumbar junction, cement volume, kyphosis correction, or intradiscal cement leakage could lead to infection after vertebroplasty. CONCLUSIONS: The present analysis demonstrated that low bone mineral density, the presence of multiple treated vertebrae and a history of steroid usage were associated with the new VCFs after vertebroplasty. Patients with these factors should be informed of the potential increased risk.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.047 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".