From age to frailty: redefining chronic pain characterization
Bibliographic record
Abstract
BACKGROUND: Chronic pain in older adults is highly prevalent, multifactorial, and often associated with greater intensity, multisite involvement, and functional impairment. Despite its burden, it remains frequently underdiagnosed and undertreated. Chronological age alone does not adequately capture biological vulnerability or interindividual variability in pain expression. AIMS: To examine the associations of frailty, an indirect marker of biological age, and chronological age with chronic pain characteristics. METHODS: We conducted a cross-sectional study including 455 adults (≥18 years) recruited from primary care. Thirty-three pain characteristics were assessed through structured interviews. Frailty was quantified using a 31-item Frailty Index based on the deficit accumulation model. Associations of frailty, chronological age, and sex with each pain variable were analyzed using multivariable linear and logistic regression models. RESULTS: Most pain characteristics were more consistently associated with frailty than with chronological age, although effect sizes were modest (sr2 typically 1–5%). Frailty correlated with greater pain intensity (sr 0.23, r2 5.3%), higher frequency (sr 0.10, r2 1.1%), and continuous or mixed-type pain (OR 0.97, 95% CI 0.95–0.99). In contrast, chronological age primarily predicted temporal aspects, including pain duration, diagnostic delay, and time to first analgesic prescription. Age and frailty showed opposite directions of association for certain domains, such as accompanying symptoms and daily pain duration. CONCLUSION: Frailty provides complementary information to chronological age in characterizing chronic pain. Integrating frailty assessment into routine pain evaluation may enable more individualized management, enhance pain control, and reduce age-related disparities in clinical care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".