Risk factors of in⁃stent restenosis for intracranial artery stenosis: a Meta⁃analysis
Bibliographic record
Abstract
Objective To assess the risk factors of in-stent restenosis (ISR) for intracranial artery stenosis by Meta-analysis. Methods Retrieve relevant case-control studies or cohort studies from online databases (January 1, 1990-August 1, 2017) as PubMed, EBMASE/SCOPUS and Cochrane Library with key words: intracranial artery, stent, restenosis, risk factors, predictors. Selection of studies was performed according to pre-designed inclusion and exclusion criteria. Quality of studies was evaluated by using Newcastle-Ottawa Scale (NOS). All data were pooled by RevMan 5.3 software for Meta-analysis. Results The research enrolled 305 articles, from which 16 high-quality (NOS score>=6) studies were chosen after excluding duplicates and those not meeting the inclusion criteria. A total of 1102 cases (ISR:N =245; non-ISR: N=857) were included. Meta-analysis showed that diabetes (OR=1.880, 95%CI:1.290-2.740; P=0.001), lesions stenosis length>10 mm (OR=3.550, 95%CI:1.160-10.850; P=0.030), anterior circulation lesions (OR=1.680, 95%CI:1.170-2.420; P=0.005), postoperative residual stenosis>=30% (OR=3.290, 95%CI:1.460-7.410; P=0.004) and bare metal stents (OR=4.290, 95%CI:1.130-16.260; P=0.030) increased the risk of ISR significantly. Conclusions Diabetes, lesions stenosis length>10 mm, anterior circulation lesions, postoperative residual stenosis>=30% and bare metal stents were risk factors of in-stent restenosis. Clinicians should avoid related risk factors and reduce the occurrence of in-stent restenosis.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.062 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".