Early Neurological Deterioration After Recanalization: A Retrospective Study of 1053 Endovascular Treatment Patients
Bibliographic record
Abstract
BACKGROUND: Many acute ischemic stroke patients experience poor functional outcomes despite successful recanalization following endovascular treatment (EVT). We aimed to identify predictive factors for early neurological deterioration (END) following successful EVT in acute ischemic stroke patients and to evaluate the impact of END on clinical outcomes. METHODS: One thousand fifty-three patients who achieved successful recanalization (modified treatment in cerebral infarction 2b-3) were divided into two groups: without END (No-END, n = 942) and with END (n = 111). Baseline characteristics, pretreatment Alberta Stroke Program Early CT Score (ASPECTS), onset-to-puncture time, atrial fibrillation (AF), and parenchymal hematoma (PH) were analyzed. Binary regression analysis was performed to identify independent predictors of END. Favorable functional outcomes (modified Rankin scale 0-2) and mortality were assessed at 3 months. RESULTS: The END group had a lower prevalence of AF (26% vs. 36%, P = 0.026), lower pre-EVT ASPECTS scores (median 8 vs. 9, P < 0.001), longer onset-to-puncture time (median 360 vs. 290 minutes, P = 0.002), and a higher incidence of PH (39% vs. 10%, P < 0.001) compared to the No-END group. Binary regression analysis identified lower pre-EVT ASPECTS (odds ratio [OR]=0.742, P = 0.001), absence of AF (OR=0.575, P = 0.024), and presence of PH (OR=5.373, P < 0.001) as independent predictors of END. At 3 months, the END group had lower favorable outcomes (6% vs. 56%, P < 0.001) and higher mortality (63% vs. 13%, P < 0.001). CONCLUSIONS: Lower pre-EVT ASPECTS scores, absence of AF, and presence of PH are independent predictors of END following successful recanalization. END is associated with significantly worse clinical outcomes, including lower rates of favorable functional outcomes and higher mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".