Imaging Improves Efficacy of Vertebroplasty – A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Clinical trials with percutaneous vertebral augmentation (PVA) for intractable pain from vertebral compression fractures (VCF) have shown variable results. Variation in the outcomes may be related to poor patient selection on imaging. OBJECTIVE: To assess if PVA augmentation for osteoporotic VCF results in better improvement in pain when patients were selected based on clinical examination plus imaging vs clinical examination only. RESULTS: A systematic review and meta-analysis were performed. PubMed, Embase and Cochrane Library databases were searched from 2000 to May 2018. Two reviewers independently screened and extracted data to identify randomised control trials (RCTs) on PVA for osteoporotic VCF and assessed the risk of bias. Standard systematic review and meta-analysis methods were advocated by the Cochrane Collaboration and PRISMA Statement. A total of 12 RCTs with 1110 participants met the inclusion criteria. Eight of the 10 studies (938 participants) that used imaging to confirm oedema in the target vertebral bodies showed PVA (compared to nonsurgical treatment) was effective in reducing pain (immediate term: mean difference (MD) of -1.89; 95% confidence interval -1.93 to -1.85, p < 0.001; short term: MD of -1.68; 95% CI -1.82 to -1.54, p < 0.001; intermediate term: MD of -2.04; 95% CI -2.15 to -1.94, p < 0.001 and long term: MD of -1.88; 95% CI -1.95 to -1.80, p < 0.001). CONCLUSIONS: RCTs using imaging to confirm marrow oedema in the index vertebra showed an improved size effect compared to RCTs using no imaging. This benefit was observed in the immediate, short, intermediate and long term.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".