Pain and Mortality in Older Adults: The Influence of Pain Phenotype
Bibliographic record
Abstract
OBJECTIVE: Moderate to severe chronic pain affects 1 in 5 adults. Pain may increase the risk of mortality, but the relationship is unclear. This study investigated whether mortality risk was influenced by pain phenotype, characterized by pain extent or pain impact on daily life. METHODS: The study population was drawn from 2 large population cohorts of adults ages ≥50 years, the English Longitudinal Study of Ageing (n = 6,324) and the North Staffordshire Osteoarthritis Project (n = 10,985). Survival analyses (Cox's proportional hazard models) estimated the risk of mortality in participants reporting any pain and then separately according to the extent of pain (total number of pain sites, widespread pain according to the American College of Rheumatology [ACR] criteria, and widespread pain according to Manchester criteria) and pain impact on daily life (pain interference and often troubled with pain). Models were cumulatively adjusted for age, sex, education, and wealth/adequacy of income. RESULTS: After adjustments, the report of any pain (mortality rate ratio [MRR] 1.06 [95% confidence interval (95% CI) 0.95-1.19]) or having widespread pain (ACR 1.07 [95% CI 0.92-1.23] or Manchester 1.16 [95% CI 0.99-1.36]) was not associated with an increased risk of mortality. Participants who were often troubled with pain (MRR 1.29 [95% CI 1.12-1.49]) and those who reported quite a bit of pain interference (MRR 1.38 [95% CI 1.20-1.59]) and extreme pain interference (MRR 1.88 [1.54-2.29]) had an increased risk of all-cause mortality. CONCLUSION: Pain that interferes with daily life, rather than pain per se, was associated with an increased risk of mortality. Future studies should investigate the mechanisms through which pain increases mortality risk.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".