Predictors of functional dependence at one year in acute ischemic stroke with large vessel occlusion
Bibliographic record
Abstract
BACKGROUND: In China, the current status of clinical treatment of eLVO and the factors affecting its long-term prognosis are unclear. OBJECTIVE: This study aims to explore the predictive factors of functional outcomes at one year in patients of acute ischemic stroke with emergent large vessel occlusion (eLVO). METHODS: We retrospectively collected 536 patients who underwent treatments for eLVO. Primary outcomes included one-year functional outcomes and delayed functional independence (DFI). The logistic regression was performed to predict the primary outcome. RESULTS: 431 (85%) survivors participated in the one-year follow-up. In the multivariate logistic analysis adjusted for baseline characteristics, the following factors were found to be significant predictors of functional dependence at one year: old age (aOR = 1.042, 95% CI=1.01-1.076, p = 0.011), low Alberta stroke program early CT score (ASPECTS) (aOR = 0.791, 95% CI=0.671-0.933, p = 0.005), unsuccessful reperfusion (aOR = 0.168, 95% CI=0.048-0.586, p = 0.005), poor medication compliance (aOR = 0.022, 95% CI=0.007-0.072, p < 0.001), and complicated with stroke-associated pneumonia (SAP) (aOR = 2.269, 95% CI=1.103-4.670, p = 0.026). We also found that men (aOR = 3.947, 95% CI=1.15-13.549, p = 0.029) had better medication adherence (aOR = 14.077, 95% CI=1.736-114.157, p = 0.013), and going to rehabilitation centers (aOR = 5.197, 95% CI=1.474-18.327, p = 0.010) were independent predictors of DFI. CONCLUSION: The significant predictors of functional dependence at one year were: old age, low ASPECTS, unsuccessful reperfusion, poor medication adherence, and combination with SAP. Men, good medication adherence, and going to rehabilitation centers contributed to getting delayed functional independence.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".