Diffusion Tensor Imaging in Acute Ischemic Stroke: Usefulness of Fractional Anisotropy
Bibliographic record
Abstract
Background: Progressing stroke (PS) variably develops from initially the same size and severity, and is most frequently observed in lacunar infarctions. We investigated fractional anisotrophy (FA), mean diffusivity (MD) and infarct volume by using diffusion tensor imaging during the acute phase of ischemic stroke to determine whether these parameters are useful in characterizing and predicting PS. Methods: In this study, 55 consecutive patients admitted within 24 hours of the onset of their first ischemic stroke were included. NIH stroke scale (NIHSS) and Canadian Neurological scale (CNS) were performed upon admission, twice a day, and at discharge. Modified Rankin scale and Barthel index were also evaluated. PS was defined as a 2-point drop in NIHSS and a 1-point drop in CNS from admission to day 3. A correlation analysis was performed between clinical scale scores and imaging parameters, and the distribution of those values was compared between the two groups with and without PS. Results: Significant correlations were observed between clinical scale scores and infarct volumes. The FA ratio in 14 patients with PS was lower than the patients without PS (p=0.004). Other characteristics including infarct volume and MD ratio were not different. The FA ratio remained as an independent predictor of PS (OR, 1.055; p=0.011). Conclusions: In acute ischemic stroke within the first 24 hours, only infarct volume was correlated with clinical status. However, patients with PS showed lower FA values, which accounts for rapid and severe vasogenic edema involving the disruption of the cell membrane and axonal fibers. Moreover, FA may be a predictor of PS. J Korean Neurol Assoc 24(3):221-230, 2006
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".