Impact of <scp>ASPECTS</scp> on computed tomography angiography source images on outcome after thrombolysis or endovascular therapy in large vessel occlusions
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Endovascular therapy (ET) is superior to intravenous thrombolysis (IVT) in selected patients with anterior circulation large vessel occlusions. However, it is unclear if this positive effect also applies to patients with extensive early ischaemic changes. The aim of this study was to analyze the impact of the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) on the CT angiography source images (SI) on outcome after ET or IVT. METHODS: Using our prospectively obtained stroke database and the admission SI-ASPECTS divided into three groups (0-5, 6-7 and 8-10), primarily the rates of good outcome [modified Rankin Scale (mRS) ≤2 at discharge] after either ET (n = 255) or IVT (n = 479) were compared. RESULTS: A favorable SI-ASPECTS (8-10) was present in 501 patients, 132 patients had a moderately favorable SI-ASPECTS (6-7) and 101 patients had an unfavorable SI-ASPECTS (0-5). Irrespective of the treatment modality, no patient with an unfavorable SI-ASPECTS had a good outcome and 38% died during hospital stay. Whilst significantly more patients with a favorable SI-ASPECTS had a good outcome after ET than after IVT (51% vs. 35%, P < 0.01), there was only a non-significant trend towards a good outcome after ET than after IVT in patients with a moderately favorable ASPECTS (25% vs. 14%, P = 0.1). CONCLUSION: Patients with extensive early ischaemic changes on CT scans (SI- ASPECTS ≤5) might not profit from ET. The impact of ET on outcome in patients with moderately favorable SI-ASPECTS should be addressed in further trials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".