Analysis of DWI ASPECTS and recanalization outcomes of patients with acute-phase cerebral infarction.
Bibliographic record
Abstract
In order to rapidly judge the response to intravenous tissue plasminogen activator (Ⅳ tPA) treatment, we retrospectively analyzed clinical data, such as MRI diffusion-weighted images (DWI), and treatment outcomes in 73 patients who developed anterior circulation disorders. The patients with favorable outcomes (modified Rankin Scale [mRS]: 2 or less) at discharge accounted for 32.9%. In these patients, the National Institutes of Health Stroke Scale (NIHSS) value, DWI Alberta Stroke Programme Early CT Score (ASPECTS), and the incidence of large artery (internal carotid artery [ICA]/sphenoidal segment of the middle cerebral artery [M1]) occlusion at their hospital visit were lower, higher, and lower, respectively (all P < 0.05 in univariate analysis). Multivariate analysis showed significant differences in DWI ASPECTS and the incidence of large artery occlusion. A DWI ASPECTS of at least 8 was found to be predictive of favorable outcomes. However, subclass analysis in the group with a DWI ASPECTS of 8 or higher predicting favorable outcome revealed 13 patients (41.9%) with unfavorable (mRS, 3-6) outcome. The factor associated with unfavorable outcomes is ICA occlusion. The combination of DWI ASPECTS and MRA appeared to be useful for predicting outcomes of Ⅳ tPA.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".