Abstract WMP116: Incidental Diffusion-weighted Imaging Lesions In Patients With Cognitive Decline
Bibliographic record
Abstract
Objectives: Incidental diffusion-weighted imaging (DWI) hyperintense lesions on MRI, indicative of incident silent infarcts, are not uncommon in patients with advanced cerebral small vessel disease (SVD). SVD has been increasingly recognized as playing an important role in cognitive decline in the elderly. We thus examined the prevalence and associated risk factors for DWI hyperintense lesions in a cohort of patients followed in a memory clinic. Methods: We retrospectively analyzed MRI scans from 251 patients (43% females, mean age 73.3±8.3) with cognitive impairment enrolled in an ongoing prospective longitudinal study at the Massachusetts Alzheimer’s Disease Research Center (MADRC) between 2001 and 2010. Clinical and demographic data including the presence of specific vascular risk factors and degree of cognitive impairment (as measured by the Clinical Dementia Rating (CDR) Scale) were recorded. DWI and apparent diffusion coefficient (ADC) images were inspected for the presence of DWI-hyperintense lesions. Associated MRI markers of SVD including white matter hyperintensities (WMH), whole-brain mean global ADC, cerebral microbleeds (CMB) and lacunar infarcts were quantified. Results: We identified 16 DWI-hyperintense lesions in 13 (5.2%) patients. The 14 individuals with at least one DWI lesion did not differ from the 237 without lesions by age, gender, vascular risk factors, CDR, moderate to severe WMH, mean global ADC or number of CMB (p>0.05). Assuming a 10-day post-stroke period when DWI lesions remain hyperintense, their estimated annual prevalence would be 2.3 new infarcts per person-year. Conclusions: Incident DWI-hyperintense lesions, consistent with subacute cerebral infarcts, are not uncommon in unselected patients with cognitive decline. These lesions do not appear to be associated with other markers of SVD nor with degree of impairment. Our results suggest that the cumulative lifetime burden of these lesions may be substantial. These findings may support a role for small infarcts in age-related cognitive impairment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".