Brain perfusion SPECT imaging and acetazolamide challenge in vascular cognitive impairment
Bibliographic record
Abstract
Cerebrovascular disease is recognized as a common cause of cognitive impairment and dementia, alone or coexisting with other neurodegenerative diseases, mostly Alzheimer's disease. Vascular cognitive impairment (VCI) is a part of the heterogenous disorders group related to cerebral vessel disease. Although age is one of the most important risk factors for VCI, other common cardiovascular risk factors are also involved. By investigating these risk factors, a high proportion of these cognitive disorders can be prevented and/or delayed. Until now, only treatment of midlife arterial hypertension has been recognized as a preventing factor of vascular dementia. Brain MRI is becoming the method of choice to investigate cerebral vascular pathologies. However, this form of morphological imaging remains inadequate and does not provide useful functional information during VCI exploration, despite which functional imaging such as brain perfusion single-photon computed tomography, performed in baseline conditions and/or after an acetazolamide challenge, is underutilized in VCI exploration. The common strategies for VCI screening have not been standardized until now, and therefore further long-term imaging studies are needed to establish early diagnostic protocols. The present review summarizes the potential benefits of brain perfusion single-photon computed tomography imaging and possible scintigraphic quantification of cerebral hemodynamic reserves in investigation of VCI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".