Analysis of Influencing Factors for Chronic Low Back Pain with Cognitive Impairment
Bibliographic record
Abstract
Background: Cognitive impairment (CI) is a common complication in chronic low back pain (CLBP) patients, and its progression increases the risk of dementia. However, there is currently a lack of predictive indicators for CLBP-CI. Previous studies have shown that routine blood indexes have predictive value for Alzheimer’s disease, but their relationship with CLBP-CI remains unclear. This study aims to explore the correlation between routine blood indexes and provide evidence of disparities in chronic pain and cognitive impairment between two groups of individuals with low back pain, as well as establish the foundation for longitudinal experimental studies aimed at developing effective interventions for cognitive impairment in individuals with chronic low back pain. Methods: This cross-sectional study was conducted at West China Hospital, Sichuan University. The Montreal Cognitive Assessment (MoCA) was conducted to divide patients into the CLBP-CI or CLBP-nCI group. Statistical analysis was performed to examine the differences between chronic low back pain patients with cognitive impairment and those without cognitive impairment. All statistical tests were conducted at a significance level of α=0.05 for two-sided testing. Results: The prevalence of chronic low back pain with cognitive impairment in this study demonstrates age-related disparities, with a higher prevalence observed among older individuals (P=0.009). A statistically significant difference in white blood cell count was observed between individuals with chronic low back pain and cognitive impairment (P=0.004). Conclusion: Age and white blood cell count may serve as influential factors in the development of chronic low back pain with cognitive impairment. This finding can aid healthcare professionals in implementing early intervention and treatment for individuals experiencing this condition.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".