Changes in cognition of ADRD patients: effects of social isolation proxies, utilising data from UK electronic health records
Bibliographic record
Abstract
Abstract Background Marital status and living status are components of social isolation (SI), a modifiable factor thought to impact cognitive resilience, which has the potential to impact cognition throughout the course of Alzheimer’s and related dementia (ADRD) diagnosis. Electronic health records (EHRs) offer access to large scale clinical data, capable of longitudinal analyses. Method Cognitive function measurement – Montreal Cognitive Assessment (MoCA) – data, demographic (including marital and living status as SI proxies) data and ADRD diagnosis data from patients aged 50+ years from Oxford Health NHS Foundation Trust (UK) were extracted using natural language processing algorithms from EHRs dated 1995 to 2022. Longitudinal multilevel models were used to predict cognition as a function of the interaction between diagnosis duration and SI proxies, controlling for age, sex and diagnosis cause. Result ‘Lifelong single’ marital status significantly predicted reduced cognition intercept scores for the MoCA dataset (𝛽 = ‐1.61, SE = 0.67, t = ‐2.42, p = 0.016). No significant marital status predictors for slope were found. Living in supported accommodation significantly predicted steeper slopes for cognition (𝛽 = ‐2.37, SE = 0.33, t = ‐7.20, p < 0.001). No living status levels significantly predicted slopes. Conclusion Worldwide ADRD incidence is predicted to increase dramatically within the next 30 years, therefore studies investigating the impact of modifiable factors on the rates of cognitive change in ADRD patients are valuable to enhancing understanding of patient care. SI data extracted from EHRs can be used to predict differences in patient cognition scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".