Risk factors for in-stent restenosis of vertebral artery origin after stent implantation: a Meta-analysis
Bibliographic record
Abstract
Objective To systematically review the risk factors for in-stent restenosis (ISR) of vertebral artery origin after sent implantation to provide theoretical foundation for clinical prevention and treatment. Methods Taking vertebral artery, vertebrobasilar insufficiency, stents, drug-eluting stents, self expandable metallic stents in English and Chinese as key words, retrospective clinical studies about risk factors for ISR of vertebral artery origin were searched by using PubMed, EMBASE/SCOPUS, Cochrane Library, China Biology Medicine (CBM), China National Knowledge Infrastructure (CNKI), Wanfang Data and VIP database from January 1, 1966 to March 30, 2017. Quality assessment and Meta-analysis were made by using Newcastle-Ottawa Scale (NOS) and Stata 12.0 software. Results The research enrolled 3468 articles in all, from which 11 studies were chosen after excluding duplicates and those not meeting the inclusion criteria. A total number of 1352 patients were divided into ISR group (N = 440) and non-ISR group (N = 912). The ISR incidence rate of smokers was significantly higher than non-smokers (OR = 2.179, 95%CI: 1.373-3.458; P = 0.001). The differences of bare metal stents (BMS) utilization rate (OR = 2.072, 95% CI: 1.560-2.753; P = 0.000) and drug-eluting stents (DES) utilization rate (OR = 0.483, 95% CI: 0.363-0.641; P = 0.000) between ISR group and non-ISR group were statistically significant. Conclusions Smoking and using BMS are risk factors for ISR of vertebral artery origin, and using DES is protective factor. Due to limited study quality, more high-quality studies are needed to verify this conclusion. DOI: 10.3969/j.issn.1672-6731.2017.12.004
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".