Abstract P56: Diffusion Tensor Imaging at the Time of Stroke is Associated With Cognitive Performance 4 Months Later
Bibliographic record
Abstract
Introduction: Cognitive impairment after stroke is associated with stroke severity and baseline brain health. We hypothesized that acute diffusion tensor imaging (DTI) metrics would identify patients at risk for post-stroke cognitive impairment. Methods: Patients were enrolled prospectively in an observational study that involves serial MRI and cognitive testing in patients with recent stroke and moderate white matter disease on MRI but without dementia. DTI was performed at the time of stroke; cognitive testing with the MOCA and the Telephone Interview for Cognitive Status (TICS) were performed 3 months later. DTI was used to calculate Peak Skeletonized Mean Diffusivity (PSMD), a measure of global white matter microstructural integrity previously validated in cerebral small vessel disease. Fractional anisotropy maps were skeletonized (figure panel A) and a histogram of the corresponding MD values was used to calculate the peak width in the non-stroke hemisphere (panel B). Linear regression was used to test whether acute PSMD in the non-stroke hemisphere, acute stroke volume, or baseline NIHSS predicted cognitive performance 3 months later. Results: Fourteen patients followed-up at a median of 123 days. Patients had a median age of 73 years, mean baseline NIHSS of 1.2 (IQR 0-1.75), mean infarct volume of 4cc (range 0-16cc), mean MOCA of 25 (range 19-30), mean TICS of 33 (range 23-41), and 50% were women. Using multivariable linear regression, only acute PSMD predicted follow-up MOCA (std beta= -0.64, adj R 2 = 0.37, p= 0.013) while compared to baseline NIHSS, PSMD showed a stronger association with follow-up TICS score (std beta= -0.57 vs -0.44, p= 0.017; model adj R 2 = 0.476, p= 0.011)(Panel C). Conclusions: In this cohort of patients with small strokes we found that acute contralateral PSMD provided a measure of brain health that appears to predict cognitive performance at 3 months better than stroke size or severity. These are preliminary findings from an ongoing study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".