Integrating Natural Language Processing to Assess Effect of Loneliness and Social isolation on Cognitive Decline in Dementia
Bibliographic record
Abstract
BACKGROUND: Social isolation and loneliness are recognized as priority public health problems, showing impact on dementia development and progression. METHOD: Reports of social isolation, loneliness, and Montreal Cognitive Assessment (MoCA) scores were extracted from dementia patients' medical records using bespoke Natural Language Processing models and analyzed using mixed-effects models. RESULT: Lonely patients (n = 382) showed lower MoCA scores throughout the disease (B = -0.83, t = -2.64, p = 0.008). Socially isolated patients (n = 523) experienced faster cognitive decline six months before diagnosis (B = -0.21, t = -2.18, p = 0.029), but were comparable to controls (n = 3912) before this period. This led to lower MoCA scores at diagnosis (B = -0.69, t = -2.53, p = 0.011) and in later stages. CONCLUSION: Lower cognitive levels in lonely and socially isolated patients suggest that these factors may contribute to dementia progression.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".