O-001 Impact of Recanalization in Patients with Pretreatment DWI-ASPECTS ≤6 Treated with Endovascular Therapy
Bibliographic record
Abstract
Background and purpose In acute ischemic stroke (AIS) patients, a diffusion-weighted imaging (DWI) Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is correlated with infarct volume and is an independent factor of functional outcomes. Patients with pretreatment DWI-ASPECTS ≤6 were excluded or underrepresented in the recent randomized endovascular therapy (EVT) trials. Our aim was to assess the impact of recanalization in patients with pretreatment DWI-ASPECTS ≤6 treated with EVT. Methods We analyzed data collected between January 2012 and August 2015 in 2 prospective clinical registries of AIS patients treated with EVT. Every patient with a documented internal carotid artery or middle cerebral artery occlusion with pretreatment DWI-ASPECTS ≤6 was eligible for this study. The primary outcome was a favorable outcome defined by a modified Rankin Scale of 0 to 2 at 90 days. Results Two hundred eighteen patients were included. Among them, 145 (66%) had a good recanalization (TICI ≥ 2 b) at the end of EVT. There was no statistically difference in the baseline clinical characteristics between recanalised and non-recanalised patients. Recanalized patients had an increased rate of favorable outcomes (38.7% vs 17.4%, p = 0.002) and a decreased rate of mortality at 3 months (22.5% vs 39.1%, p = 0.013) compared with non-recanalised patients. The symptomatic intracerebral hemorrhage rate was not different in the 2 groups (13% vs 14.1%, p = 0.83). Conclusion Patients with a pretreatment DWI-ASPECTS ≤6 may still benefit of EVT when a good recanalization is achieved. In particular, EVT-induced recanalization was associated with a reduced rate of mortality without increased risk of symptomatic intracerebral hemorrhage. Disclosures J. Desilles: None. A. Consoli: None. S. Escalard: None. H. Redjem: None. R. Blanc: None. P. Guedin: None. O. Coskun: None. G. Ciccio: None. S. Smajda: None. C. Ruiz Guerrero: None. P. Sasannejad: None. G. Rodesch: None. M. Piotin: None. B. Lapergue: None.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".