Risk factors for pulmonary cement embolism associated with percutaneous vertebral augmentation: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Pulmonary cement embolism (PCE) was a rare but fatal complication for percutaneous vertebral augmentation (PVA). Thus we did a systematic review and meta-analysis of cohort studies to investigate the risk factors for PCE after PVA. METHODS: We systematically searched PubMed, EMBASE, Cochrane library, Google Scholar, web of science, and ClinicalTrial.gov from the establishment of the database to September 2021. All eligible studies assessing the risk factors for PCE after PVA were incorporated. Dichotomous data was calculated by risk difference (RD) from Mantel-Haenszel method (M - H method); continuous data was analyzed by mean difference (MD) from Inverse-Variance method (I-V method). All variables were taken as measure of effect by fixed effect model. Heterogeneity, sensitivity, and publication bias analyses were also performed. RESULTS: This study totally included 13 studies. According to the Newcastle-Ottawa Scale (NOS), 7 studies were considered as low quality, with NOS< 6. The others were of relatively high quality, with NOS≥6. 144/6251 patients (2.3%) had PCE after PVA. percutaneous vertebroplasty (PVP) (RD = 0.02, 95%CI: [0.01, 0.04], Z = 3.70, P < 0.01), thoracic vertebra (RD = 0.03, 95%CI: [0.01, 0.05], Z = 3.53, P < 0.01), higher cement volume injected per level (MD = 0.23, 95%CI: [0.05, 0.42], Z = 2.44, P = 0.01), more than three vertebrae treated per session (MD = -0.05, 95%CI: [-0.08, -0.02], Z = 3.65, P < 0.01), venous cement leakage (RD = 0.07, 95%CI: [0.03, 0.11], Z = 3.79, P < 0.01) were more likely to cause PCE. CONCLUSION: This study showed that risk factors for PCE included PVP, thoracic vertebra, higher cement volume injected per level, more than three vertebrae treated per session, venous cement leakage. As a serious complication, PCE should be paid attention and avoided.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.003 | 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".