Mechanical thrombectomy first versus direct angioplasty or stenting for the treatment of intracranial atherosclerotic stenosis-related large vessel occlusion: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Mechanical thrombectomy (MT) using stent retrievers or a direct aspiration first-pass technique has proven to yield better results over intravenous thrombolysis in treating acute ischaemic stroke caused by large vessel occlusion (LVO). However, the treatment of intracranial atherosclerotic stenosis-related LVO remains unclear and has been a critical problem in daily clinical practice, as it can cause a relatively high failure rate for MT. Whether direct angioplasty and/or stenting is clinically feasible and shows advantage in reducing delay to revascularisation with better functional outcome compared with MT with rescue angioplasty and/or stenting remains unclear. This study seeks to provide direct and practical clinical evidence for clinicians. Methods and analysis The main databases of PubMed, the Cochrane library, Embase and Web of Science will be screened for related studies published after1 January 2015. Primary outcomes include successful recanalisation and 90-day favourable outcome. Secondary outcomes include puncture to revascularisation time, vascular complication (perforation, dissection and vasospasm), intracerebral haemorrhage, hospital-related complications and 90-day mortality. The Newcastle-Ottawa Scale will be adopted to assess risk bias of observational studies. The I 2 statistic will be used to assess heterogeneity. Ethics and dissemination No primary data of patients are needed. Therefore, ethics approval is unnecessary. The results of this systematic review and meta-analysis will be published in a peer-reviewed journal. PROSPERO registration number CRD42021268061.
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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 | 0.051 | 0.065 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.026 | 0.030 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.004 |
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".