Differences between predictive factors for early neurological deterioration due to hemorrhagic and ischemic insults following intravenous recombinant tissue plasminogen activator
Bibliographic record
Abstract
Abstract Early neurological deterioration (END) following intravenous recombinant tissue plasminogen activator (rt-PA) treatment is a serious clinical event that can be caused by hemorrhagic or ischemic insult. We investigated the differences in predictive factors for END due to hemorrhagic and END due to ischemic insults. Consecutive patients from four hospitals who received 0.6 mg/kg intravenous rt-PA for acute ischemic stroke were retrospectively recruited. END was defined as a National Institutes of Health Stroke Scale (NIHSS) score ≥ 4 points within 24 h compared with baseline. END was classified into those due to hemorrhagic (END h ) or ischemic (END i ) insult based on computed tomography (CT) or magnetic resonance imaging. Risk factors associated with END h and END i were investigated by comparison with non-END cases. A total of 744 patients (452 men, median 75 years old) were included. END was observed in 79 patients (10.6%), including 22 END h (3.0%) and 57 END i (7.7%), which occurred within a median of 7 h after treatment. Multivariate analyses showed that higher pretreatment NIHSS score (odds ratio [OR] 1.06, 95% confidence interval [CI] 1.00–1.13) and pretreatment with antiplatelets (OR 2.84, 95% CI 1.08–7.72) were associated with END h . Extensive early ischemic change (Alberta Stroke Program Early CT Score ≤ 7 on CT or ≤ 6 on diffusion-weighted imaging; OR 2.80, 95% CI 1.36–5.64) and large artery occlusions (OR 3.09, 95% CI 1.53–6.57) were associated with END i . Distinct factors were predictive for the END subtypes. These findings could help develop preventative measures for END in patients with the identified risk factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".