Visuospatial impairment in dementia with Lewy bodies and Alzheimer's disease: a process analysis approach
Bibliographic record
Abstract
BACKGROUND: Reports of differential impairments on visual-construction tasks in dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) are sometimes controversial, whereas visual-perceptual data are lacking. The existence of different clinical sub-groups of DLB has been hypothesized to explain the discrepancies among the cognitive results. The goal of this study was to compare the visual-perceptual performance of subjects with DLB with predominant psychosis, DLB with predominant parkinsonian features and AD. METHODS: This is a cross-sectional neuropsychological study with between diagnostic group comparisons. The Benton Judgement Line Orientation (BJLO) test was administered to four DLB patients with predominant psychosis (DLB-psy), four DLB subjects with predominant parkinsonian features (DLB-PD), and 13 patients with AD. An analysis of error types was applied to the results of the BJLO with QO1, QO2, QO3, QO4 (visual attention) errors, as well as VH, IQO, IQOV, and IQOH (visual-spatial perception) errors. RESULTS: A MANOVA showed significant differences between the DLB, and AD groups on the number of VH (F = 6.049, df = 1,19, p = 0.024), IQOH (F = 4.645, df = 1,19, p = 0.044) and QO1 (F = 4.491, df = 1,19, p = 0.047) errors, but no difference on the total score of the BJLO. Another MANOVA and post hoc Student-Newman-Keuls analyses demonstrated that the DLB-psy sub-group made significantly more VH and IQOH errors than AD and the DLB-PD subjects. CONCLUSIONS: Subjects with DLB and psychosis have more severe visual-perception (VH errors) impairments than subjects with DLB and predominant parkinsonian features, and AD subjects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".