Abstract P500: Outcomes of Mechanical Thrombectomy in Patients With Low Aspects: Insights From Star
Bibliographic record
Abstract
Introduction: Patients with poor baseline images were excluded from most clinical trials so the data about whether these patients could benefit from MT remains unknown. In this study, we aim to investigate the safety and efficacy of MT in patients with large vessel occlusion (LVO) and large core infarct (LCI). Methods: The Stroke Thrombectomy and Aneurysm Registry (STAR) was interrogated. We included thrombectomy patients presenting with LVO within 24 hours and with a LCI as defined by Alberta Stroke Program Early CT Score (ASPECTS) < 6. Patients presenting within 6 hours of last known normal (LKN) were considered in the early window and patients presenting after 6 hours were considered in the late window. 90-day outcomes were assessed. We used a logistic regression model to assess the factors associated with good 90-day outcome in patients in the early and late windows. Results: 144 patients were included in this study (table). Median age was 69 and 92 (64%) patients were treated in the early MT window. ICA was the most common site of occlusion (48.6%) and ADAPT was used in 34.7%. Admission NIHSS was 17.5. Successful recanalization (TICI>2b) was achieved in 84.7% and median procedure time was 54 minutes. sICH hemorrhage was observed in 22 (15.3%). Median mRS was 4 at 90 days. Favorable outcome was observed in 41 patients (28.5%) and mortality occurred in in 59 (41%). There was no difference in 90-day functional outcome between patients in early and late windows. In patients presenting in the early window, age (aOR=0.905, p=0.0002) and baseline NIHSS (aOR=0.909, p=0.0423) were independently associated with 90-day outcome. In patients presenting in the late window, only age (aOR=0.934, p=0.0069) was independently associated with good outcome. Conclusion: More than one in four patients presenting with ASPECTS<6 may achieve functional independence at 90-day following MT. Patient age remains the main predictor of 90-day outcome in patients with low ASPECTS in both late and early windows.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".