Association of Cognitive Impairment and Spinal Pain in the Older Adult Population in the United States: A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to explore the association between cognitive impairment and spinal pain in the older population in the United States. METHODS: We undertook a secondary analysis of cross-sectional data from the 1999 to 2000 and 2001 to 2002 National Health and Nutrition Examination Survey. The pooled data included a representative sample (n = 2975) of older adults (aged 60-85 years) in the United States. Cognitive impairment was assessed through the Digit Symbol Substitution Test. Spinal pain was defined with a multisite definition, including both nonspecific low back pain and neck pain present in the past 3 months. To account for the complex sampling design, logistic regression was performed using Taylor linearized variance estimation to compute weighted measures of associations. RESULTS: For older adults with spinal pain, the proportion of cognitive impairment increased with age, from 32.64% in the 60 to 64 age group to 93.83% in the 80 to 84 age group, which was also statistically significantly higher than the general population group and the group without spinal pain (P < .001). After controlling for demographic characteristics, socioeconomic status, and general health status, older adults with spinal pain had significantly increased odds of cognitive impairment (odds ratio 1.76, 95% confidence interval: 1.12, 2.79). Vulnerable subgroups (older, female, and less education) were identified. CONCLUSION: There was a significant association between cognitive impairment and spinal pain in the older adult population in the United States. Within this population, there were vulnerable subgroups for which spinal pain and cognitive impairment had a greater impact, namely people who are older, female, and those with less education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.001 | 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".