Novel selection paradigms for endovascular stroke treatment in the extended time window
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The optimal selection methodology for stroke thrombectomy beyond 6 hours remains to be established. METHODS: Review of a prospectively collected database of thrombectomy patients with anterior circulation strokes, adequate CT perfusion (CTP) maps, National Institute of Health Stroke Scale (NIHSS)≥10 and presenting beyond 6 hours from January 2014 to October 2018. Patients were categorised according to five selection paradigms: DAWN clinical-core mismatch (DAWN-CCM): between age-adjusted NIHSS and CTP core, DEFUSE 3 perfusion imaging mismatch (DEFUSE-3-PIM): between CTP-derived perfusion defect (Tmax >6 s lesion) and ischaemic core volumes and three non-contrast CT Alberta Stroke Program Early CT Score (ASPECTS)-based criteria: age-adjusted clinical-ASPECTS mismatch (aCAM): between age-adjusted NIHSS and ASPECTS, eloquence-adjusted clinical ASPECTS mismatch (eCAM): ASPECTS 6-10 and non-involvement of the right M6 and left M4 areas and standard clinical ASPECTS mismatch (sCAM): ASPECTS 6-10. RESULTS: 310 patients underwent analysis. DEFUSE-3-PIM had the highest proportion of qualifying patients followed by sCAM, eCAM, aCAM and DAWN-CCM (93.5%, 92.6%, 90.6%, 90% and 84.5%, respectively). Patients meeting aCAM, eCAM, sCAM and DAWN-CCM criteria had higher rates of 90-day good outcome compared with their non-qualifying counterparts(43.2% vs 12%,p=0.002; 42.4% vs 17.4%, p=0.02; 42.4% vs 11.2%, p=0.009; and 43.7% vs 20.5%, p=0.007, respectively). There was no difference between patients meeting DEFUSE-3-PIM criteria versus not(40.8% vs 31.3%,p=0.45). In multivariate analysis, all selection modalities except for DEFUSE-3-PIM were independently associated with 90-day good outcome. CONCLUSIONS: ASPECTS-based selection paradigms for late presenting and wake-up strokes ET have comparable proportions of qualifying patients and similar 90-day functional outcomes as DAWN-CCM and DEFUSE-3-PIM. They also might lead to better outcome discrimination. These could represent a potential alternative for centres where access to advanced imaging is limited.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".