Predictors for hemorrhagic transformation with intravenous tissue plasminogen activator in acute ischemic stroke.
Bibliographic record
Abstract
We examined the predictive value of clinical and radiological findings, including cerebral microbleeds (CMBs) seen in gradient-echo T2*-weighted magnetic resonance images, for hemorrhagic transformation (HT) following ischemic stroke, in ischemic stroke patients treated with recombinant tissue plasminogen activator (rt-PA). The subjects were 71 patients with acute ischemic stroke treated with rt-PA (50 males, 21 females; mean age±standard deviation 73±10 years; 53 cardiogenic stroke, 18 atherothrombotic). HT on computed tomography (CT)(mean: 24 hours after onset) was seen in 26 (37%) subjects. The mean Alberta stroke programme early CT score on diffusion-weighted images (ASPECTS-DWI) score was significantly lower in the group with HT than that in the group without HT (6.5±2.3 vs 8.4±1.6, P<0.001). Prevalence of CMBs was not significantly different between the groups with and without HT. Relative risk of various factors for appearance of HT was evaluated by logistic regression analysis. Increased ASPECTS-DWI score showed a significantly reduced relative risk for HT (odds ratio: 0.54, 95% confidence interval: 0.33-0.87), while the influence of CMBs (1.22, 0.23-6.53) was not significant. In conclusion, ASPECTS-DWI score (a measure of the volume of ischemic tissue) is a useful marker for predicting HT. On the other hand, CMBs on T2*-weighted images may not be predictive for HT in patients treated with intravenous rt-PA.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".