Role of neuroimaging in multidisciplinary approach towards Non-Alzheimer’s dementia
Bibliographic record
Abstract
Dementia is defined as chronic deterioration of intellectual function and cognitive skills significant enough to interfere with the ability to perform daily activities. Recent advances in the treatment of dementia have renewed interest in the use of various neuroimaging techniques that can assist in the diagnosis and differentiation of various subtypes. Neuroimaging and computational techniques have helped the radiological community to monitor disease progression of various neurodegenerative conditions presenting with dementia, such as Alzheimer disease, frontotemporal lobe dementia (FTLD), progressive supranuclear palsy (PSP) and multisystem atrophy-cerebellar variant (MSA-C), and their response to newer therapies. Prompt identification of treatable or reversible forms of dementia, such as tumours, subdural haemorrhage and intracranial dAVF, is crucial for the effective management of these conditions. It is also prudent to recognize the imaging spectrum of metabolic, infective and autoimmune diseases with rapidly progressing dementia, such as methanol toxicity, central pontine myelinolysis (CPM), delayed post hypoxic leukoencephalopathy (DPHL), HIV, Creutzfeldt-Jakob Disease (CJD), Sjogren's syndrome, multiple sclerosis (MS), radiation necrosis and Fragile X-Associated Tremor/Ataxia Syndrome (FXTAS), which are difficult to treat and often require palliative care. This pictorial review emphasizes various non-Alzheimer's dementia entities and discusses their imaging highlights. Teaching Points • Non Alzheimer's dementia constitutes a broad spectrum of conditions. • Neuroimaging plays an important role in differentiating treatable from irreversible dementia. • Neuroimaging is often non-specific in early stages of neurodegenerative conditions with dementia. • Neuroimaging plays an important role in the multimodal approach towards management of dementia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".