Predictors of Poor Outcome after Successful Mechanical Thrombectomy in Patients with Acute Anterior Circulation Stroke
Bibliographic record
Abstract
Abstract Successful revascularization is one of the main predictors of a favorable clinical outcome after mechanical thrombectomy. However, even if mechanical thrombectomy is successful, some patients have a poor clinical outcome. This study aimed to investigate the clinical, imaging, and procedural factors that are predictive of poor clinical outcomes despite successful revascularization after mechanical thrombectomy in patients with acute anterior circulation stroke. The authors evaluated 69 consecutive patients (mean age, 74.6 years, 29 women) who presented with acute ischemic stroke due to internal cerebral artery or middle cerebral artery occlusions and who were successfully treated with mechanical thrombectomy between July 2014 and November 2016. A good outcome was defined as a modified Rankin Scale score of 0 to 2 at 3 months after treatment. The associations between the clinical, imaging, and procedural factors and poor outcome were evaluated using logistic regression analyses. Using multivariate analyses, the authors found that the preoperative National Institute of Health Stroke Scale (NIHSS) score (odds ratio [OR], 1.152; 95% confidence interval [CI], 1.004–1.325; p = 0.028), the diffusion-weighted imaging Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS) (OR, 0.604; 95% CI, 0.412–0.882; p = 0.003), and a Thrombolysis in Cerebral Infarction (TICI) 2b classification (OR, 4.521; 95% CI, 1.140–17.885; p = 0.026) were independent predictors of poor outcome. Complete revascularization to reduce the infarct volume should be performed, especially in patients with a high DWI-ASPECTS, to increase the likelihood of a good outcome.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".