Brain volume loss precedes cognitive decline in the first year after ischaemic stroke
Bibliographic record
Abstract
Objectives The aim of the current study was to examine trajectories of brain volume and cognition in ischaemic stroke patients in the first year after their events and compare results with age-matched controls. Methods In the Cognition And Neocortical Volume After Stroke (CANVAS) study, we assessed ischaemic stroke patients within six weeks of their events and at 12 months post-stroke, and compared their results to healthy, age-matched controls. All participants were without dementia. Brain volume was assessed with high-resolution 3T MPRAGE MRI. FreeSurfer v5.1 was used to obtain total brain, hippocampal and thalamic volumes. The Hopkins Verbal Learning Test-Revised (HVLT-R) was used to assess memory, and the computerised Cogstate Battery was used to assess processing speed, working memory and attention. Results Sixty-five stroke patients (51 male; age 65.98±11.6 years, education 12.71±3.9 years; baseline NIHSS 3.3±2.6; days post-stroke 26.83±9) and 38 controls (23 male; age 68.6±6.8; education 15.68±4.5 years) completed their assessments at baseline and 12 months. Oxfordshire classifications were PACI 31; POCI 25; LACI 9. Brain volumes were averaged across hemispheres for controls. Between baseline and 12 months, rates of decline in total brain volume, ipsilesional thalamic volume and ipsilesional hippocampal volume were significantly greater in stroke patients relative to controls (p<0.05). Contralateral brain volume loss in ischaemic stroke patients was not different to controls (p>0.05). Rates of cognitive decline over the same period were comparable between groups (p>0.05). Conclusions Brain volume loss precedes cognitive decline in the first year after ischaemic stroke. Tracking brain volume after stroke is important because brain volume is predictive of cognitive decline and atrophy is a hallmark of dementia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".