Modelling the impact of social isolation on the rate of cognitive decline of dementia patients
Bibliographic record
Abstract
Abstract Background The current study looked to model the impact of social isolation (SI), using marital status as a proxy, on the rate of cognitive decline of dementia patients. It was hypothesised that patients with higher levels of SI (whose marital statuses reflected less socially connected living conditions) would exhibit steeper rates of cognitive decline over time than patients with lower levels of SI, and patients with higher levels of SI would have lower intercept cognitive scores compared to patients with lower levels of SI. Method Patient demographic, diagnosis, cognitive assessment (Mini‐Mental State Examination [MMSE]/Montreal Cognitive Assessment [MoCA]) and marital status data were collated from electronic health records. Linear mixed‐effects models were used to explore the main effects and interaction effects of SI on the rate of cognitive decline of 4,137 patients from date of diagnosis to up to five years after (17,131 total observations). Result Comparisons for the MMSE and MoCA models showed the main effect models significantly outperformed null models testing the fixed effect of only diagnosis duration. The models revealed a similar rate of cognitive decline across all marital statuses. Excluding divorced patients, average intercept scores for less isolated marital statuses (married, civil‐partnership cohabiting) were higher than more isolated marital statuses (single, separated, widowed) for the MMSE models, but not the MoCA models. Interestingly, divorced patients had the highest average intercept scores in both the MMSE and MoCA models. Conclusion Future research should look to develop the SI proxy using data from the COVID‐19 pandemic to explore the implications of isolation through enforced social restrictions on cognitive decline in dementia patients.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".