Can smaller lacunes derived from recent small subcortical infarcts play a role in cognition at one- year after mild stroke?
Bibliographic record
Abstract
Recent small subcortical infarcts (RSSI) may evolve into lacunes (cavities) smaller than 3mm or even disappear. The 3mm size cut-off used in guidelines might underestimate SVD burden. We hypothesised that participants with smaller (<3mm) lacunes have better cognitive outcomes at one-year follow-up than those with larger lacunes. We also aimed to determine rates of development of lacunes <3mm. We recruited participants from two prospective stroke cohorts (MSS2 and MSS3) within 3-months after mild stroke. We included participants with MRI-confirmed RSSI and at least two MRI scans during the first one-year follow-up. We assessed for lesion change by visual assessment on T2- FLAIR (blinded). We recorded demographics, vascular risk factors, SVD burden, and clinical outcomes (NIHSS, modified Rankin score [mRS], Montreal Cognitive Assessment score [MoCA]), at baseline and one-year. We report maximum axial diameters (max-ax, mm) for RSSI and lacunes (continuous and dichotomised at < /≥3mm). We used regression analysis for associations between final lacune size/appearance and outcomes at one-year, adjusting for baseline demographics, VRF, and clinical scores. We included 198 participants; mean age 64 years (SD 11.1); 33% female. At one-year, 53/184 (26.8%) RSSI evolved into lacunes <3mm and 105/184 in to lacunes over 3mm (Table.1) Participants with lacunes <3mm had higher MoCA (MoCA<26; RR=0.57 [95%CI 0.33, 0.97]; vs 1.35 [1.05-1.75] for larger lacunes; p=0.03) and lower mRS (mRS 0-1; RR=1.79[1.11,2.91] vs 0.72[0.58-0.89]; p=.009). The end-stage lacune size correlated with RSSI max-ax diameter at baseline (r[df1]=[0.73],p<.001); there were no associations with demographics, VRF or SVD burden. At one-year, 47/143 (23.7%) participants had MoCA<26, and we investigated the effects of age, NIHSS, NART, RSSI max-ax diameter, SVD burden and MoCA at baseline and end-stage lacune max-axial diameter in this group. MoCA at baseline was a significant predictor for cognition at one-year (β=0.586, SE=0.90 [95%CI: 0.41, 0.76], p<.001). MoCA scores were lower in those with larger end-stage lacunes (β=-1.950, SE=0.70 [95%CI: 0.04, 0.56], p=0.005). Larger end-stage lacune diameters are associated with worse cognitive outcomes at one-year after mild stroke. Careful cognitive and lesion assessment of patients at diagnosis may help determine cognitive trajectories in patients with mild stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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 teacher head, 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".