Is there a benefit of mechanical thrombectomy in patients with large stroke (<scp>DWI</scp>‐<scp>ASPECTS</scp> ≤ 5)?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Whether to withhold mechanical thrombectomy when the diffusion-weighted imaging (DWI) lesion exceeds a given volume is undetermined. Our aim was to identify markers that will help to select patients with large DWI lesions [DWI-Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS) ≤ 5] that may benefit from thrombectomy. METHODS: From May 2010 to November 2016, 82 acute ischaemic stroke patients with DWI-ASPECTS ≤5 (43 men, 64.6 ± 14.4 years, National Institutes of Health Stroke Scale 18.4 ± 5.4) treated with state-of-the-art mechanical thrombectomy were studied. Thrombectomy alone was performed in 28 (34%) and bridging therapy in 54 (66%) patients. Recanalization was defined as a thrombolysis in cerebral infarction score 2B-3 and significant hemorrhagic transformation as parenchymal haematoma type 2 (European Cooperative Acute Stroke Study 3 classification). Pretreatment variables were compared between patients with a good (modified Rankin Scale 0-2) and a poor (modified Rankin Scale 3-6) neurological outcome at 3 months. RESULTS: Overall, 28 patients (34%) achieved good neurological outcome at 3 months. Recanalizers were significantly more likely to achieve good outcome (61% vs. 7.3%, P < 0.0001), had lower mortality (24% vs. 49%, P = 0.03) and similar rates of parenchymal haematoma type 2 (9.8% vs. 7.3%, P = 1) compared to non-recanalizers. Regression modelling identified DWI-ASPECTS >2 [odds ratio (OR) 6.93; 95% confidence interval (CI) 1.05-45.76, P = 0.04), glycaemia ≤6.8 mmol/l (OR 4.05; 95% CI 1.09-15.0, P = 0.03) and thrombolysis (OR 3.67; 95% CI 1.04-12.9, P = 0.04) as independent predictors of good neurological outcome. CONCLUSIONS: In patients with DWI-ASPECTS ≤5, two-thirds of patients experienced good neurological outcome when recanalized by state-of-the-art thrombectomy, whilst only one in 14 non-recanalizers achieved similar outcomes. Pretreatment markers of good neurological outcomes were DWI-ASPECTS >2, intravenous thrombolysis and glycaemia ≤6.8 mmol/l.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".