NEUROCOGNITIVE CORRELATES OF COMORBIDITIES IN OLDER ADULTS WITH CHRONIC PAIN AND NEGATIVE EMOTIONS
Bibliographic record
Abstract
Abstract Chronic pain and depression are associated with poor cognition in older adults, yet little is known regarding the additional contribution of comorbidities to performance on specific neurocognitive domains in this population. We examined specific neurocognitive domain performance using baseline data from the Problem Adaptation Therapy for Pain Collaborative Care Program (PATH-Pain) trial, which tested a psychosocial intervention designed to improve emotion regulation in 100 adults ≥ 60 years with comorbid chronic pain and negative emotions (vs. usual care). Participants completed questionnaires on comorbidities (Charlson Comorbidity Index), depressive symptoms (Montgomery-Asberg Depression Rating Scale), pain intensity, and were remotely administered the Montreal Cognitive Assessment with no visual elements (MoCA-blind) and the Repeatable Battery for Neuropsychological Status. Regression assessed the relationship between specific neurocognitive domains and comorbidities, adjusting for depressive symptoms and pain intensity. In adjusted models, greater comorbidities remained associated with poorer immediate memory (b = -.352, p <.001) adjusting for pain intensity (b =.217, p =.040) and depressive symptoms (ns). After excluding participants with neurologic conditions, immediate memory remained associated with comorbidities (b = -.304, p =.019), but not pain intensity. Similar patterns emerged for attention, with greater comorbidities associated with poorer performance among all participants (b = -.266, p =.015), and for participants without neurological diagnoses (b = -.301, p =.020). No associations were found with language, delayed memory, abstraction, or orientation. Findings suggest difficulties with initial encoding and verbal learning; further research is needed to understand the potential impact on health behaviors and outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".