Relationships Between Cognitive Screening Composite Scores and Pain Intensity and Pain Disability in Adults With/At Risk for Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: Chronic pain, cognitive deficits, and pain-related disability are interrelated. The prevalence of chronic pain and undiagnosed cognitive difficulties in middle age and older adults is increasing. Of the cognitive systems, executive function and episodic memory are most relevant to chronic pain. We examined the hypothesis that cognitive screening composite scores for executive function and memory would negatively associate with pain intensity and pain disability in a group of middle-aged and older adults with knee pain with or at risk for osteoarthritis. METHODS: A total of 120 adults (44 men/76 women), an average age of 59 years, participated in the study. Demographic, health history, clinical pain, and cognitive measures were completed. Relationships between pain intensity, pain disability, and the Montreal Cognitive Assessment (MoCA) total and composite scores were examined with relevant covariates in the model. RESULTS: MoCA raw scores ranged from 13 to 30 with a mean score of 23.9. Pain intensity was negatively associated with overall MoCA total and executive function and memory composite scores. Pain disability over the previous 6 months was negatively associated with executive function, while pain disability over the past 48 hours was not associated with executive function. CONCLUSION: The results of the current study demonstrates associations between pain metrics and cognitive domain scores within a common cognitive screening tool.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".