PREDICTORS OF POOR OUTCOME AFTER THROMBECTOMY IN ACUTE ISCHEMIC STROKE PATIENTS
Bibliographic record
Abstract
Objective: Timely and effective recanalization of the occluded vessel is of importance for acute ischemic stroke patients. However, Successful recanalization (SR) is not always associated with good prognosis. We aimed to explore predictive factors of poor outcome of successful recanalization after thrombectomy in patients with acute anterior circulation large-vessel occlusion.Method: Between January 2016 and October 2018, the eligible patients with SR were retrospectively enrolled. Poor outcome was defined as modified Rankin Scale (mRS) of 3 to 6 at 90 days. We used univariate and multivariate logistic regression analysis to explore risk factors of poor outcome.Results: We enrolled 76 patients with SR (mean age: 64.34 u00b1 14.90, 46 males). The proportion of patients with poor outcome was 57.9% (44/76). The multivariable logistic regression showed systolic blood pressure (SBP) (OR, 1.03; 95% CI, 1.00-1.07; P=0.041), baseline National Institutes of Health Stroke Scale (NIHSS) score (OR, 1.17; 95% CI, 1.04-1.31; P=0.007 ), and blood glucose levels (OR, 1.80; 95% CI, 1.09u20132.96; P=0.022 ) were the predictive factors of poor outcome, while baseline Alberta Stroke Program Early CT Score (ASPECTS) was the protective factor. (OR, 0.49; 95% CI, 0.33u20130.73; P<0.001). Conclusion: High SBP, high NIHSS, high blood glucose and low ASPECTS were associated with poor outcome despite successful recanalization after thrombectomy in patients with acute ischemic stroke. Further large sample studies are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".