Profile of Cognitive Complaints in Vascular Mild Cognitive Impairment and Mild Cognitive Impairment
Bibliographic record
Abstract
Objective. Vascular mild cognitive impairment (VaMCI) is differentiated from mild cognitive impairment (MCI) by the presence of vascular events such as stroke or small vessel disease. Typically, MCI and VaMCI patients present with subjective complaints regarding cognition; however, little is known about the specific nature of these complaints. We aimed to create a profile of subjective cognitive complaints in MCI and VaMCI patients with similar levels of objective cognitive performance. Methods. Twenty MCI and twenty VaMCI patients were recruited from a Memory Disorders Clinic in Toronto. Subjective cognitive complaints were assessed and categorized using the Neuropsychological Impairment Scale. Results. MCI and VaMCI patients achieved similar scores on measures of objective cognitive function (P > 0.100). However, the VaMCI group had more subjective complaints than the MCI group (P = 0.050), particularly in the critical items, cognitive efficiency, memory, and verbal learning domains of the Neuropsychological Impairment Scale. Conclusions. Our findings support the idea that VaMCI and MCI differ in their clinical profiles, independent of neuroimaging. VaMCI patients have significantly more subjective cognitive complaints and may be exhibiting particular deficits in memory, verbal learning, and cognitive efficiency. Our findings promote the need for further research into VaMCI-specific cognitive deficits.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".