Relationships Between Chronic Pain Stage, Cognition, Temporal Lobe Cortex, and Sociodemographic Variables
Bibliographic record
Abstract
BACKGROUND: Non-Hispanic black (NHB) individuals have increased risk of Alzheimer's disease (AD) relative to non-Hispanic whites (NHW). Ethnicity/race can serve as a proxy sociodemographic variable for a complex representation of sociocultural and environmental factors. Chronic pain is a form of stress with high prevalence and sociodemographic disparities. Chronic pain is linked to lower cognition and accelerated biological aging. OBJECTIVE: The purpose of this study is to seek understanding of potential cognitive and temporal lobe structural brain AD vulnerabilities based on chronic pain stage and ethnicity/race. METHODS: Participants included 147 community dwelling NHB and NHW adults without dementia between 45-85 years old who had or were at risk of knee osteoarthritis. All participants received an MRI (3T Philips), the Montreal Cognitive Assessment (MoCA), and assessment of clinical knee pain stage. RESULTS: There were ethnic/race group differences in MoCA scores but no relationships with chronic knee pain stage. Ethnicity/race moderated the relationship between AD-related temporal lobe thickness and chronic pain stage with quadratic patterns suggesting thinner cortex in high chronic pain stage NHB adults. CONCLUSION: There appear to be complex relationships between chronic knee pain stage, temporal lobe cortex, and sociodemographic variables. Specifically, NHB participants without dementia but with high chronic knee pain stage appeared to have thinner temporal cortex in areas associated with AD. Understanding the effects of sociocultural and socioeconomic factors on health outcomes is the first step to challenging the disparities in healthcare that now appear to link disease conditions to neurodegenerative processes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".