Intracranial stenting compared to medical treatment alone for intracranial atherosclerosis patients: An updated meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Stroke is the second-leading cause of death globally. Intracranial atherosclerotic stenosis (ICAS) represents 10-15% of ischemic strokes in Western countries and up to 47% in Asian countries. Patients with ICAS have an especially high risk of stroke recurrence. The aim of this meta-analysis is to reassess recurrent stroke, transient ischemic attack (TIA), and other outcomes with stenting versus best medical management for symptomatic ICAS. METHODS: The search protocol was developed a priori according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The OVID Medline, Embase, Web of Science, and Cochrane Library databases were searched from inception to August 14th, 2022. RESULTS: This Meta-analysis Included Four Randomized Controlled Trials (Rcts), With A Total Number Of 991 Patients. The Mean Age Of Participants Was 57 Years. The Total Number Of Intracranial Stenting Patients Was 495, And The Number Of Medical Treatment Patients Was 496. The Included Studies Were Published Between 2011 And 2022. Two Studies Were Conducted In The Usa, And The Other Two In China. All Included Studies Compared Intracranial Stenting To Medical Treatment For Icas. CONCLUSIONS: In patients with ischemic stroke due to symptomatic severe intracranial atherosclerosis, the rate of 30-day ischemic stroke, 30-day intracerebral hemorrhage, one-year stroke in territory or mortality favored the medical treatment alone without intracranial stenting. The risk of same-territory stroke at last follow-up, disabling stroke at last follow-up, and mortality did not significantly favor either group. Intracranial stenting for atherosclerosis did not result in significant benefit over medical treatment.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.062 |
| Bibliometrics | 0.006 | 0.005 |
| 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".