Correlates of neurocognitive performance in older adults with chronic pain and negative emotions: baseline data from the problem adaptation therapy for pain (PATH-pain) randomized controlled trial
Bibliographic record
Abstract
Chronic pain is highly prevalent among older adults, is associated with cognitive deficits, and is commonly treated in primary care. We sought to document the extent of impairment across specific neurocognitive domains and its correlates among older adults with chronic pain in primary care. We analyzed baseline data from the Problem Adaptation Therapy for Pain trial, which examined a psychosocial intervention to improve emotion regulation in 100 adults ≥ 60 years with comorbid chronic pain and negative emotions, who did not have evidence of moderate-to-severe cognitive impairment. Questionnaires on comorbidities, depressive symptoms, pain intensity, and pain-related disability were administered along with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and the Montreal Cognitive Assessment (MoCA). Multiple regression assessed the relationship between demographic and clinical characteristics with specific neurocognitive domains. Over half of participants (56%) had mild-to-moderate cognitive impairment (<26 on the MoCA). Across domains, participants scored the lowest in visuospatial/constructional (M = 86.2; SD = 15.7), and 15%–23% scored at least one standard deviation below the mean for immediate and delayed memory, visuospatial/constructional, and attention. In adjusted models, greater medical comorbidities were associated with poorer performance on the total RBANS, immediate memory, and attention. Cognitive deficits in older adults with chronic pain in primary care are substantial, with varying levels of deficits by neurocognitive domain. Future research should examine synergistic effects of chronic pain and comorbidities on cognition, and the impact of cognitive deficits on older adults' ability to engage in pain interventions and self-management behaviors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".