Success with incrementally faster times to endovascular therapy (SWIFT-EVT): A systematic review and meta-analysis
Bibliographic record
Abstract
Background A major systematic review and meta-analysis assessing trial data through 2014 (the Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials, HERMES) demonstrated that particularly over the initial six hours of acute ischemic stroke (AIS), rapid performance of endovascular therapy (EVT) markedly improves outcomes. The current analysis, Success with Incrementally Faster Times to EVT (SWIFT-EVT), aimed to provide an updated metric summarizing latest estimates for modified Rankin Scale (mRS) improvements accrued by streamlining time to EVT. Methods A systematic review and meta-analysis was conducted using electronic databases. Eligible studies reported a time-benefit slope with times from AIS onset (or time last known normal) to EVT commencement; the predictor was onset-to-groin (OTG) time. Primary and secondary outcomes were 90-day functional independence (mRS 0-2) and 90-day excellent function (mRS 0-1), respectively. Results Five studies were included. Results showed increased change of good outcome with each hour of pre-EVT time savings for mRS 0-2 for 0-270' (OR 1.25, 95 % CI 1.16-1.35, I 2 40 %) and 271-360' time frame (1.22, 95 % CI 1.12-1.33, I 2 58 %). For the studies assessing mRS 0-1, estimates were found appropriate for both the 0-270' time frame (OR 1.34, 95 % CI 1.19-1.51, I 2 27 %) and the 271-360' time frame (OR 1.20, 95 % CI 1.03-1.38, I 2 60 %). Conclusions Each hour saved from AIS onset to EVT start is associated with a 22-25 % increased odds of achieving functional independence, a useful metric to inform patient-specific and systems planning decisions.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.043 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".