Stroke- on- Awakening: Safety of CT-CTA Based Selection for Reperfusion Therapy
Bibliographic record
Abstract
BACKGROUND: We studied the safety of use of acute reperfusion therapies in patients with stroke- on- awakening using a computed tomographic angiography (Cta) based large vessel occlusion-good scan paradigm in clinical routine. METHODS: the Cta database of the Calgary stroke program was reviewed for the period January 2003-March 2010. patients with stroke-on-awakening with large artery occlusions on Cta, who received conservative, iV thrombolytic and/or endovascular treatment at discretion of the attending stroke neurologist were analyzed. time of onset was defined by the time last seen or known to be normal. Baseline non-contrast Ct scan (nCCt) alberta Stroke program early Ct Score (aSpeCtS) > 7 was considered a good scan. hemorrhage was defined on follow-up brain imaging using eCaSS 3 criteria. independence (mrS≤2) at three months was considered a good clinical outcome. Standard descriptive statistics and multivariable analysis were done. RESULTS: among 532 patients with large artery occlusions, 70 patients with stroke-on-awakening (13.1%) were identified. the median age was 69.5 (iQr 24) and 41 (58.6%) were female; 41 (58.6%) received anti-platelets only and 29 (41.4%) received thrombolytic treatment [iV-12 (17.1%), iV/ia-12 (17.1%) and ia-5(7.1%)]. unadjusted analysis showed that baseline nCCt aSpeCtS ≤ 7 (p=0.002) and higher nihSS scores (p=0.018) were associated with worse outcomes. there were no ph2 hemorrhages in the iV thrombolytic or endovascular treated group. functional outcome was not different by treatment. CONCLUSION: When carefully selected using Ct –Cta, by a good scan (aSpeCtS > 7) occlusion paradigm, acute reperfusion therapies in patients with stroke-on-awakening can be performed safely in clinical routine.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".