Risk factors for hemodynamic depression after carotid artery stenting: A system review and meta analysis
Bibliographic record
Abstract
PURPOSE: Hemodynamic depression (HD) regularly occurs during carotid artery stenting(CAS) for treating carotid stenosis and it could result in adverse clinical events. This review aimed to clarify the incidence and risk factors for HD. METHODS: We searched four comprehensive databases for studies that reported the incidence or risk factors for HD during CAS. We used a modified version of Newcastle-Ottawa scale to assess the risk of bias in the included studies. We pooled the prevalence rates of HD and related risk factors from individual studies with a generic inverse variance weighted using the randomized effects model. We reported the results using OR with 95 % CI. Funnel plots and Egger's tests were used to assess the publication bias. RESULTS: Our meta-analysis enrolled 53 articles and revealed that the incidence of HD was 35 %. Patients who had diabetes (OR = 1.28, 95 % CI: 1.07 to 1.54), stenosis-to-bifurcation <10 mm (OR = 2.11, 95 % CI: 1.29 to 3.48), stenosis involving the carotid bulb (OR = 1.9, 95 % CI: 1.07 to 3.38), calcified plaque (OR = 2.06, 95 % CI: 1.32 to 3.22), eccentric plaque (OR = 1.47, 95 % CI: 1.05 to 2.05), severe stenosis (OR = 1.64, 95 % CI: 1.1 to 2.43), contralateral stenosis (OR = 2.02, 95 % CI: 1.18 to 3.46), open-cell stents (OR = 1.5, 95 % CI: 1.04 to 2.15), and bilateral stenting (OR = 2.32, 95 % CI: 1.56 to 3.44) showed a higher risk of HD. CONCLUSIONS: Diabetes, stenosis-to-bifurcation <10 mm, stenosis involving the carotid bulb, calcified plaque, eccentric plaque, severe stenosis, contralateral stenosis, open-cell stents, and bilateral carotid stenting were associated with HD during CAS.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| 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".