Pain and the Montreal Cognitive Assessment (MoCA) in Aging
Bibliographic record
Abstract
OBJECTIVE: The present study aimed to determine whether specific cognitive domains part of the Montreal Cognitive Assessment (MoCA) are significantly lower in community-dwelling older adults with chronic pain compared with older adults without pain and whether these domains would be associated with self-reported pain, disability, and somatosensory function. DESIGN: Secondary data analysis, cross-sectional. SETTING: University of Florida. SUBJECTS: Individuals over 60 years old enrolled in the Neuromodulatory Examination of Pain and mobility Across the Lifespan (NEPAL) study were included if they completed the MoCA and other study measures (n = 62). Most participants reported pain on most days during the past three months (63%). METHODS: Subjects underwent a health assessment (HAS) and a quantitative sensory testing (QST) session. Health/medical history, cognitive function and self-reported pain measures were administered during the HAS. Mechanical and thermal detection, and thermal pain thresholds were assessed during the QST session. RESULTS: Older adults with chronic pain had lower MoCA scores compared with controls on domains of executive function, attention, memory, and language (P < 0.05). The attention and language domains survived adjustments for age, sex, education, depression, and pain duration (P < 0.05). Attention was significantly associated with all pain characteristics including pain intensity and disability, while executive function was associated with mechanical detection (P < 0.05). CONCLUSION: Our results support previous findings that individuals with chronic pain tend to show poorer cognitive functioning compared with pain-free controls in domains of attention and executive function. Our findings also extend these findings to community-dwelling older adults, who are already most vulnerable to age-related cognitive declines.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".