Modelling cognitive decline: the impact of social isolation and loneliness on the cognitive trajectories of Alzheimer’s disease and related diseases patients
Bibliographic record
Abstract
This thesis focuses on the impact of social isolation and loneliness on cognition and cognitive trajectories of patients with an Alzheimer’s disease or related disease diagnosis. While social isolation and loneliness are known to impact incidence risk, their effect on cognitive trajectories in patients, particularly after diagnosis, is relatively under studied. As these factors are potentially modifiable, exploring their impact offers an opportunity to inform care plans or non-pharmacological interventions, thereby improving quality of life for patients. A retrospective cohort design of electronic healthcare records was used across three modelling studies. Study 1 aimed to develop proxies of social isolation from the records and model their impact using linear multilevel models. Study 2 looked to build upon the models from Study 1 by introducing the addition of non-linear multilevel models. Study 3 looked to further develop the models by introducing a natural language processing algorithm to detect novel proxies of both social isolation and loneliness from the records and analyse their impact on cognitive outcomes using a combination of linear and non-linear models. Findings indicated that accommodation status was a strong predictor of cognitive scores at diagnosis, regardless of cognitive measure. Reports of loneliness and social isolation predicted significant yet differing impacts on cognition as measured by the Montreal Cognitive Assessment. Patients experiencing loneliness exhibited worse overall cognition. Whereas, patients experiencing social isolation exhibited initially similar cognitive trajectories as controls with rates of cognitive decline that increased, relatively, around diagnosis. These studies demonstrate that social and demographic factors related to social isolation and loneliness contribute to cognitive performances across diagnosis trajectories and therefore have practical implications for clinical screening and routine data collection at memory clinics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".