Eligibility for late endovascular treatment using DAWN, DEFUSE-3, and more liberal selection criteria in a stroke center
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The real-life application of DAWN and DEFUSE-3 trials has been poorly investigated. We aimed to identify the proportion of patients with acute ischemic stroke (AIS) eligible for late endovascular treatment (EVT) in our stroke center based on trial and more liberal selection criteria. METHODS: All consecutive patients in our stroke registry (2003-2017) admitted within 5-23 hours of last proof of good health were selected if they had complete clinical and radiological datasets. We calculated the proportion of patients eligible for late EVT according to trial (DAWN and/or DEFUSE-3) and more liberal clinical/imaging mismatch criteria (including lower admission National Institutes of Health Stroke Scale score and Alberta Stroke Program Early CT Score for core estimation). RESULTS: Of 1705 patients with AIS admitted to our comprehensive stroke center in the late time window, we identified 925 patients with complete clinical and radiological data. Among them, the proportions of late EVT eligibility were 2.5% (n=23) with DAWN, 5.1% (n=47) with DEFUSE-3, and 11.1% (n=103) with more liberal criteria. Considering late-arriving patients with large vessel occlusion (n=221), the percentages of eligible patients were 10.4%, 21.3%, and 46.6%, respectively. A favorable outcome was observed at comparable rates in treated patients selected by trial or liberal criteria (67% vs 58%, p=0.49). CONCLUSIONS: In a long-term stroke registry, the proportion of late EVT eligibility varied greatly according to selection criteria and referral pattern. Among late-arriving patients referred to our comprehensive stroke center, we found 5.6% eligible according to trial (DAWN/DEFUSE-3) and 11.1% according to liberal criteria. These data indicate that late EVT could be offered to a larger population of patients if more liberal criteria are applied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".