Clinical and radiological outcome after mechanical thrombectomy in acute ischemic stroke: What matters?
Bibliographic record
Abstract
OBJECTIVE: Recent studies have shown the efficacy of mechanical thrombectomy in acute ischemic stroke. We sought to identify prognostic parameters for clinical and radiological outcome after mechanical thrombectomy. METHODS: In 34 patients (age 72 ± 13 years, 64.7% women) with acute occlusion of the distal ICA and/or M1 segment who were treated with mechanical thrombectomy, the Spearman correlation was performed to assess potential prognostic outcome parameters (age, NIHSS, ASPECT, thrombus length (TL), clot burden score (CBS), relative filling time delay (rFTD), time to recanalization (TTR) and TICI score). The modified Rankin scale (mRS) and the Alberta Stroke Program Early CT (ASPECT) score were used for clinical and radiological outcome, respectively. Receiver operating characteristic (ROC) analysis was performed to assess parameters predicting favorable clinical (ΔmRS ≤ 2) and radiological outcome (ΔASPECT ≤ 2). RESULTS: Variables associated with favorable clinical outcome included NIHSS, TL, TTR and TICI score (p ≤ 0.01) with NIHSS ≤ 15 (p = 0.001, area under the curve (AUC) 0.87), TL ≤ 2 cm (p = 0.017, AUC 0.75), TTR ≤ 231 min (p = 0.001 AUC 0.88) and TICI ≥ 2b (p = 0.050, AUC 0.70). Shorter TTR and higher TICI scores were associated with favorable radiological outcome (p < 0.001) with TTR ≤ 224 min (p = 0.023, AUC 0.77) and TICI ≥ 2b (p = 0.000, AUC 0.86). CONCLUSION: Fast and complete recanalization is essential to achieve a favorable radiological and functional outcome after mechanical thrombectomy in acute ischemic stroke. Age, CBS and collateral supply play a subordinate role.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".