Regions of atrophy that correlate with cognitive impairment in subtypes of dementia
Bibliographic record
Abstract
Objectives: The objective of this study was to examine regions of volume loss in the different subtypes of dementia to find novel biomarkers for diagnosis of the disease in vivo. \n Methods: Sixty-three computed tomography (CT) scans of patients with diagnoses of mild cognitive impairment (MCI, n=17), Alzheimer’s disease (AD, n=15), vascular cognitive impairment (VCI, n=21) and vascular dementia (VaD, n=10) were obtained from the memory disorders clinic at St Michael’s Hospital, Toronto. In addition to cognitive evaluation carried out using the Behavioural Neurology Assessment (BNA), all patients underwent a CT scan. Linear measurements were performed to assess degree and location of atrophy and then correlated with the BNA. \n Results: Patients with MCI showed significant loss of volume in the temporal horn (p<0.01), suprasellar cistern (p<0.05) and frontal (p<0.05) regions which correlated with declining memory. Patients with AD displayed significant loss of temporal horn volume (p<0.01) and third ventricle regions (p<0.05), which correlated with memory loss. Patients with VCI displayed decreased volume in the suprasellar cistern (p<0.01), bicaudate (p< 0.05) and third ventricle (p<0.05) regions, which correlated with memory loss. Similar correlations in the suprasellar cistern (p<0.01) and bicaudate (p<0.05) regions were found in VaD, in addition to volume loss in frontal (p<0.05) regions, which correlated with declining executive function. \n Conclusion: Although the pattern of atrophy seen with MCI and AD patients was as expected, new regions of volume loss were found in patients with VCI. The existence of atrophy in the bicaudate region is a novel finding for this diagnosis, as atrophy was previously suspected in more frontal regions. This implies that volume loss in the bicaudate region can be used as a biomarker when correlated with memory to predict conversion from VCI to VaD.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".