Clinical Relevance of ‘Cap’ and ‘Track’ Development after Recent Small Subcortical Infarct
Bibliographic record
Abstract
OBJECTIVE: After a recent small subcortical infarct (RSSI), some patients develop perilesional or remote hyperintensities ('caps/tracks') to the index infarct on T2/FLAIR MRI. However, their clinical relevance remains unclear. We investigated the clinicoradiological correlates of 'caps/tracks', and their impact on long-term outcomes following RSSI. METHODS: We identified participants with lacunar stroke and MRI-confirmed RSSI from 3 prospective studies. At baseline, we collected risk factors, RSSI characteristics, small vessel disease (SVD) features, and microstructural integrity on diffusion imaging. Over 1-year, we repeated MRI and recorded 'caps/tracks' blinded to other data. We evaluated predictors of 'caps/tracks', and their association with 1-year functional (modified Rankin Scale score ≥2), mobility (Timed Up-and-Go), cognitive outcomes (Montreal Cognitive Assessment [MoCA] score <26), and recurrent cerebrovascular events (stroke/transient ischemic attack/incident infarct) using multivariable regression. RESULTS: Among 185 participants, 93 (50.3%) developed 'caps/tracks' first detected at median 198 days after stroke. 'Caps/tracks' were independently predicted by baseline factors: larger RSSI, RSSI located in white matter, higher SVD score, and higher mean diffusivity in normal-appearing white matter (odds ratio [OR] [95% confidence interval {CI}], 1.15 [1.07-1.25], 6.01 [2.80-13.57], 1.77 [1.31-2.44], 1.42 [1.01-2.03]). At 1 year, 'cap/track' formation was associated with worse functional outcome (OR: 3.17, 95% CI: 1.28-8.22), slower gait speed (β: 0.13, 95% CI: 0.01-0.25), and recurrent cerebrovascular events (hazard ratio [HR]: 2.05, 95% CI: 1.05-4.02), but not with cognitive impairment. INTERPRETATION: 'Caps/tracks' after RSSI are associated with worse clinical outcomes, and may reflect vulnerability to progressive SVD-related injury. Reducing 'caps/tracks' may offer early efficacy markers in trials aiming to improve outcome after lacunar stroke. ANN NEUROL 2025;97:942-955.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".