Using the pupillary light response to measure cerebrovascular reactivity for dementia risk assessment
Bibliographic record
Abstract
With an ageing global population, the number of people living with Alzheimer’s disease, the leading cause of dementia, is increasing and is expected to continue to increase over the next few decades, with no known cure for the disease. Because of this, there is an urgent requirement to determine a way to identify those at an increased risk for developing the disease, to enable targeted interventions. Reduced cerebrovascular reactivity (CVR) has been reported in cases of neurodegeneration, which could be due to a smooth muscle disorder. This disorder could impact the muscles that control the contraction and dilation of the pupil and could potentially be observed through an impaired pupillary light response (PLR), and it is believed that this impairment could be observed before symptoms of cognitive decline occur. The aim of this research is to investigate the pupillary light response as a method for assessing cerebrovascular reactivity, and to use this information in conjunction with genetic, demographic, lifestyle, and life-event data to generate a risk classification for Alzheimer's disease, which considers the many potential root causes for the disease. This thesis presents the first studies investigating the relationship between the PLR and CVR in healthy adults, as well as the first study to assess the PLR-CVR relationship with an integration of covariate factors relating to demographics and lifestyle factors. It also presents a comprehensive, peer-reviewed review of literature demonstrating how modifiable risk factors for Alzheimer’s disease relate to the PLR, which can inform future risk classification models using the PLR and lifestyle information to assess dementia risk. Finally, this thesis presents the first study evaluating differences in dynamic aspects of the pupillary light response in postpartum women and women who have never been pregnant, highlighting the need to consider sex-specific factors when assessing the PLR and subsequent dementia risk. With more validation conducted in larger cohort studies, the work presented in this thesis supports the PLR as a potential screening tool for estimating cerebrovascular reactivity, and potentially subsequent dementia risk screening.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".