Comparison between collateral status and DEFUSE 3 or DAWN criteria in patient selection for endovascular thrombectomy within 6−24 hours after stroke: a protocol for meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Perfusion imaging according to the DEFUSE 3 or DAWN criteria has been applied to select patients with large vascular occlusive stroke undergo endovascular therapy (EVT) in the extended time window. Emerging studies have shown that collateral blood flow-based criteria may be as effective as DEFUSE 3 and DAWN criteria for the evaluation of EVT eligibility beyond 6 hours. We will conduct a meta-analysis to compare collateral status-based criteria with DEFUSE 3 or DAWN criteria. METHODS AND ANALYSIS: We will conduct a search for the studies comparing collateral blood flow-based imaging with CT perfusion using the DEFUSE 3 or DAWN criteria in selecting patients with acute ischaemic stroke undergo EVT in the Web of Science, PubMed, EMBASE and the Cochrane Library databases between November 2017 and November 2021. We will also search the sources of grey literature, the reference lists of included studies and the newly published studies during the review period. Two investigators will independently screen the eligible studies and extract data. The study quality will be assessed by using the Newcastle-Ottawa Scale or the Cochrane risk bias tool. Stata V.17 will be used to conduct data analysis. ETHICS AND DISSEMINATION: Patient informed consent and ethics approval are not necessary as this study uses only published studies. The finding of this meta-analysis will be propagated through committee conferences or peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42021281928.
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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 | 0.069 | 0.111 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".