Dynamic cerebral autoregulation in people with mild cognitive impairment
Bibliographic record
Abstract
Altered cerebrovascular hemodynamics and low cerebral perfusion contribute to the development and progression of dementia. Dynamic cerebral autoregulation (dCA), a measure of the cerebral vasculature’s ability to buffer abrupt changes in mean arterial pressure and prevent hypoperfusion, such as during a supine-to-standing transition, have mixed results in people clinically diagnosed with mild cognitive impairment (MCI, people with objective cognitive impairment but maintained functional independence). Therefore, in 30 people with MCI, we tested the hypothesis that participants with a higher standing middle cerebral artery velocity (MCAv) at diastole (higher-velocity group) would have lower dCA values, to confer better cerebrovascular outcomes and enhanced cognitive function compared to participants with a lower MCAv at diastole (lower-velocity group). This study separated people with MCI into different diastolic MCAv groups. dCA was calculated as (MCAv nadir -MCAv supine /MCAv supine )/(MAP MCAnadir -MAP MCAsupine /MAP MCAsupine ). This work led to the identification of a dysregulated dCA in the higher-velocity group (p = 0.009) compared to the lower-velocity group despite having greater cognitive scores (p = 0.008). Elevated levels of cerebral oxygen tissue saturation (p = 0.039) and lower end-tidal carbon dioxide (p = 0.042) suggest that a favourable dCA value may be a compensatory mechanism in the neurodegenerative disease processes. The unexpected results highlight the importance of uncovering hemodynamic pathways in clinical populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".