Loneliness, social isolation, and effects on cognitive decline in patients with dementia: A retrospective cohort study using natural language processing
Bibliographic record
Abstract
Abstract INTRODUCTION The study aimed to compare cognitive trajectories between patients with reports of social isolation and loneliness and those without. METHODS Reports of social isolation, loneliness, and Montreal Cognitive Assessment (MoCA) scores were extracted from dementia patients' medical records using natural language processing models and analyed using mixed‐effects models. RESULTS Lonely patients ( n = 382), compared to controls ( n = 3912), showed an average MoCA score that was 0.83 points lower at diagnosis ( P = 0.008) and throughout the disease. Socially isolated patients ( n = 523) experienced a 0.21 MoCA point per year faster rate of cognitive decline in the 6 months before diagnosis ( P = 0.029), but were comparable to controls before this period. This led to average MoCA scores that were 0.69 MoCA points lower at diagnosis ( P = 0.011). DISCUSSION Lower cognitive levels in lonely and socially isolated patients suggest that these factors may contribute to dementia progression. Highlights Developed Natural Language Processing model to detect social isolation and loneliness in electronic health records. Patients with loneliness reports have lower Montreal Cognitive Assessment (MoCA) scores than other patients. Social isolation was related to the faster decline in MoCA scores before diagnosis. Social isolation and loneliness are promising targets for slowing cognitive decline.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".