P2‐261: A Threshold of White Matter Hyperintensities Volume Ratio Doubling the Risk of Cognitive Decline in Patients with Stroke or Transient Ischemic Attack
Bibliographic record
Abstract
The threshold of white matter hyperintensities (WMH) burden that predicts longitudinal cognitive decline remains unknown. Two hundred and forty five stroke or transient ischemic attack (TIA) patients were followed over a mean period of 37.4 months. The Montreal Cognitive Assessment (MoCA) was administered at baseline (mean 5.1 months [SD=1.1] post event) and follow-up (42.5 [1.4] months). Change in MoCA score was calculated as follow up minus baseline and significant cognitive decline was defined as a drop of ≥3 points on the MoCA over 3 years. WMH burden was expressed using 1) visual rating by Age-Related White Matter Changes Scale Global score; 2) raw WMH volume measured on MRI; 3) highest quartile of raw WMH volume; 4) WMH volume corrected for intracranial volume (ICV) to account for differences in head size; and 5) highest quartile of ICV-corrected WMH volume. WMH volume was quantified by BrainNow. The association between WMH burden and decline in MoCA was tested using multivariable binomial regression models corrected for age, sex, education and baseline MoCA scores. Highest quartile of ICV-corrected WMH volume (0.91% of ICV) was most strongly associated with MoCA decline (Odds Ratio 2.05, 95% Confidence Interval 1.08-3.90; Figure). ARWMC Global score, raw WMH volume and its highest quartile as well as linear measure of ICV-corrected WMH volume, were not significantly associated with MoCA decline.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".