The Relationship Between Medical Comorbidity and Self-Rated Pain, Mood Disturbance, and Function in Older People With Chronic Pain
Bibliographic record
Abstract
BACKGROUND: Aging is associated with greater risk for many illnesses and the prospect of multiple, concurrent disease states. Chronic pain is also very common in advanced age, and there is likely to be a relationship with comorbid burden, but few studies have examined this issue. This study tests the hypothesis that comorbid burden is associated with greater levels of self-reported pain and associated disturbance in mood and function. METHODS: Psychometric and medical data were collected from 562 patients (mean age = 76.3 years) attending a geriatric pain clinic. The number of categories endorsed on the Cumulative Illness Rating Scale (CIRS) score was used to measure accumulated comorbid burden. These groups were tested for differences in the severity of self-reported pain. The predictive capacity of comorbid burden for explaining variance in mood disturbance and functional disability was assessed after controlling for any differences in age and severity of pain. RESULTS: Over 50% of the sample had three or more comorbid problems. Groups with greater levels of comorbidity scored higher on the Present Pain Intensity Index, the sensory and affective subscales of the McGill Pain Questionnaire. Multiple regression analysis showed that the CIRS score explained a significant proportion of the variance in scores on the Geriatric Depression Scale (4.1%), Human Activities Profile (4.8%), and the physical domain of the Sickness Impact Profile (5.9%). CONCLUSION: Greater levels of comorbidity are associated with reports of more severe pain, more depressive symptoms, reduced activity levels, and higher physical impact from pain.
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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 | 0.006 |
| 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 | 0.001 |
| 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".