Perfusion CT imaging as a diagnostic and prognostic tool for dementia: prospective case–control study
Bibliographic record
Abstract
BACKGROUND: As functional changes precede structural changes in dementia, we aimed to elucidate changes on cerebral perfusion CT (PCT) for early diagnosis of dementia; and to differentiate Alzheimer's disease (AD) from vascular dementia (VaD). We also aimed to study correlation between Montreal Cognitive Assessment (MOCA) score and PCT parameters. METHODS: We conducted a prospective case-control study enrolling 25 dementia patients (15 cases of VaD, 10 cases of AD) and 25 age-matched controls. PCT was performed on a 256-slice CT scanner. Using perfusion software, colour maps were generated for cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time and time-to-peak. These colour maps were first visually inspected for any abnormalities. Subsequently, quantitative assessment of perfusion parameters was done using symmetrical freehand region of interests drawn in bilateral frontal, temporal, parietal regions, basal ganglia and hippocampi. RESULTS: Strategic infarcts were present in 93.3% cases and white matter ischaemic changes in 100% cases of VaD. A global reduction in CBF and CBV was also observed in cases of VaD; whereas these parameters were significantly lower mainly in temporoparietal regions and hippocampi of patients with AD. There was significant positive correlation between MOCA score and various perfusion parameters in both forms of dementia. CONCLUSION: PCT is a reliable imaging modality for early diagnosis of dementia and in differentiating VaD from AD. As perfusion parameters show positive correlation with MOCA score, they could be used as a surrogate marker of cognitive status in the follow-up of patients with 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".