Combined effect of ASPECTS and age on outcome of patients with large core infarction treated with mechanical thrombectomy
Bibliographic record
Abstract
BACKGROUND: Despite the remarkable effectiveness of endovascular treatment (EVT), recent randomized controlled trials indicate that up to half of patients with large core infarction have a very poor outcome (modified Rankin Scale score 5-6 at 90 days). This study investigates the combined effect of Alberta Stroke Program Early CT Score (ASPECTS) and age on very poor outcome in patients with large core infarction treated with EVT. METHODS: This subanalysis of the MAGIC registry, which is a prospective, multicenter cohort study of early treatment in acute stroke, focused on patients with ASPECTS ≤5 presenting within 24 hours of stroke onset and receiving CT followed by EVT from November 1, 2021 to February 8, 2023. Multivariable logistic regression was used to investigate the independent and joint association of ASPECTS and age with very poor outcome. RESULTS: Among the 490 patients (57.3% men; median (IQR) age 69 (59-78) years), very poor outcome occurred more frequently in those with lower ASPECTS (65.2% in ASPECTS 0-2 vs 43.4% in ASPECTS 3-5; P<0.001). The predictive value of successful recanalization for very poor outcome was significant in patients with ASPECTS 3-5 (P=0.010), but it diminished in those with ASPECTS 0-2 (P=0.547). Compared with patients with ASPECTS 3-5 and age ≤69 years, the risk of a very poor outcome increased incrementally in those with lower ASPECTS, advanced age, or both (P<0.05). Graphical plot analysis showed a significantly lower probability of very poor outcome in younger patients (≤69 years) compared with older patients (>69 years) across all ASPECTS points. CONCLUSION: These findings suggest prioritizing young patients as candidates for EVT in those with ASPECTS 0-2.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".