Predictors for clinical and functional outcomes in stroke patients with first‐pass complete recanalization after thrombectomy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The aim was to identify baseline clinical and radiological/procedural predictors and 24-h radiological predictors for clinical and functional outcomes in stroke patients obtaining complete recanalization in one pass of mechanical thrombectomy (MT) in an optimal baseline and procedural setting. METHODS: A retrospective analysis was conducted of prospectively collected data from 924 stroke patients with anterior large vessel occlusion, Alberta Stroke Program Early Computed Tomography (ASPECT) score ≥6 and pre-stroke modified Rankin Scale score 0, who started MT ≤6 h from symptom onset and obtained first-pass complete recanalization. A first logistic regression model was performed to identify baseline clinical predictors and a second model to identify baseline radiological/procedural predictors. A third model including baseline clinical and radiological/procedural predictors was performed, and a fourth model including independent baseline predictors from the third model plus 24-h radiological variables (hemorrhagic transformation [HT] and cerebral edema [CED]). RESULTS: In the fourth model, higher National Institutes of Health Stroke Scale (NIHSS) score (odds ratio [OR] 1.089) and higher ASPECT score (OR 1.292) were predictors of early neurological improvement (ENI) (NIHSS score ≤4 points from baseline or NIHSS score of 0 at 24 h), whereas older age (OR 0.973), longer procedure time (OR 0.990), HT (OR 0.272) and CED (OR 0.569) were inversely associated with ENI. Older age (OR 0.970), diabetes mellitus (OR 0.456), higher NIHSS score (OR 0.886), general anesthesia (OR 0.454), longer onset-to-groin time (OR 0.996), HT (OR 0.340) and CED (OR 0.361) were inversely associated with 3-month excellent functional outcome (mRS score 0-1), whereas higher ASPECT score (OR 1.294) was a predictor of excellent outcome. CONCLUSIONS: Higher NIHSS score was a predictor of ENI but inversely associated with 3-month excellent outcome. Older age, HT and CED were inversely associated with both good outcomes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".