White matter hyperintensities in cognitive impairment with Lewy body disease: a multicentre study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: White matter hyperintensities (WMHs) are associated with cognitive deficits and worse clinical outcomes in dementia, but rare studies have been carried out of cognitive impairment in Lewy body disease (CI-LB) patients. The objective was to investigate the associations between WMHs and clinical manifestations in patients with CI-LB. METHODS: In this retrospective multicentre cohort study, 929 patients (486 with dementia with Lewy bodies [DLB], 262 with Parkinson's disease dementia [PDD], 74 with mild cognitive impairment [MCI] with Lewy bodies [MCI-LB] and 107 with Parkinson's disease with MCI [PD-MCI]) were analysed from 22 memory clinics between January 2018 and June 2022. Demographic and clinical data were collected by reviewing medical records. WMHs were semi-quantified according to the Fazekas method. Associations between WMHs and clinical manifestations were investigated by multivariate linear or logistic regression models. RESULTS: Dementia with Lewy bodies patients had the highest Fazekas scores compared with PDD, MCI-LB and PD-MCI. Multivariable regressions showed the Fazekas score was positively associated with the scores of Unified Parkinson's Disease Rating Scale Part III (p = 0.001), Hoehn-Yahn stage (p = 0.004) and total Neuropsychiatric Inventory (p = 0.001) in MCI-LB and PD-MCI patients. In patients with DLB and PDD, Fazekas scores were associated with the absence of rapid eye movement sleep behaviour disorder (p = 0.041) and scores of Unified Parkinson's Disease Rating Scale Part III (p < 0.001), Hoehn-Yahn stage (p < 0.001) and the Montreal Cognitive Assessment (p = 0.014). CONCLUSION: White matter hyperintensity burden of DLB was higher than for PDD, MCI-LB and PD-MCI. The greater WMH burden was significantly associated with poorer cognitive performance, worse motor function and more severe neuropsychiatric symptoms in CI-LB.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".