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Clinical manifestations of small vessel disease

2014· dissertation· en· W25927232 on OpenAlexfundno aff
Minke Kooistra

Bibliographic record

VenuePLoS Pathogens · 2014
Typedissertation
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health Research
KeywordsDiseaseMedicineSubclinical infectionCognitive declineDiabetes mellitusCognitionDepression (economics)Risk factorEndothelial dysfunctionInternal medicineHyperintensityCardiologyDementiaMagnetic resonance imagingPsychiatryRadiologyEndocrinology

Abstract

fetched live from OpenAlex

Cardiovascular disease is a major health problem worldwide. However, the mortality risk in patients with cardiovascular disease has decreased due to early detection of the disease and improved treatment possibilities. The downside of increased survival rates are higher rates of long-term functional disability. Cardiovascular disease and an increased cardiovascular risk are associated with loss of function such as cognitive dysfunction, motor dysfunction, and depression. Vascular damage on brain MRI, reflected in markers of small vessel disease, is increasingly recognized as potential underlying factor for age-related function loss that is frequently found in patients with cardiovascular disease. The present thesis aimed to gain more insight in the cross-sectional, but particularly in the longitudinal relationship between risk factors, MRI markers of small vessel disease, and functional disability. In the first part of the thesis we investigated the association between risk factors, MRI markers of small vessel disease, and cognitive decline. We found that potentially modifiable risk factors (e.g. diabetes mellitus and physical activity) may have small impact on slowing down cerebral vascular damage and cognitive dysfunction in middle-aged patients already burdened with vascular disease. Cognitive dysfunction is a slowly developing process that may follow a stepwise progression over time. MRI markers of small vessel disease may be more sensitive to detect the influence of modifiable risk factors and may precede differences in cognitive decline. In part II of the thesis we were interested in the relationship of subclinical features of small vessel disease on brain MRI with motor dysfunction. We performed an explorative study to examine the association of microstructural abnormalities in specific white matter tracts with motor dysfunction in patients with type 2 diabetes mellitus with and without cognitive impairment. We found that in a small group of older patients with type 2 diabetes mellitus with slightly impaired motor performance scores, microstructural abnormalities in the corpus callosum were associated with motor performance scores in the expected directions, although the results were not statistically significant. In part III of the thesis we investigated the course of depression in patients with cardiovascular disease. 30% of these patients had an intermittent or chronic course of depression. Furthermore, of the patients with a history of vascular disease, those with cerebrovascular disease most often showed a chronic course of depression. Cerebral small vessel disease, on top of existing cardiovascular disease contributes to depression. Depression is therefore an important clinical manifestation of both large (e.g. stroke) and small vessel disease in patients with high vascular burden. Patients with cardiovascular disease may require more careful clinical monitoring and management of depressive symptoms.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.030
GPT teacher head0.303
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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