Frailty-Informed Pain Management: A Clinical Imperative Beyond Chronological Age
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a prevalent and disabling condition whose management becomes increasingly complex with aging and frailty. While chronological age often guides clinical decisions, frailty offers a more biologically grounded approach. AIMS: We sought to examine the independent associations of chronological age, frailty, and sex with pain management variables in a community-based adult population. METHODS: A cross-sectional study was conducted in 455 adults with chronic non-cancer pain. Frailty was assessed using a 31-item frailty index (FI) on the basis of the deficit accumulation model. A total of 169 pain-related variables were collected. Multivariable regression models were used to explore associations between age, FI, sex, and pain management outcomes. RESULTS: Frailty was independently associated with nonsteroidal anti-inflammatory drug (NSAID) self-medication (odds ratio [OR] 1.03, 95% confidence interval [CI]: 1.01-1.04), greater use of nonpharmacological interventions (sr = 0.13), consumption of multiple analgesic classes (including paracetamol, opioids, and adjuvants), and absence of baseline pain control (OR 0.96, 95% CI 0.93-0.98). In contrast, older age was the main negative predictor of NSAID and anxiolytic prescriptions and physiotherapy use. Notably, frailty and age showed opposite associations for several outcomes, including number of prescribed analgesics and healthcare utilization. CONCLUSIONS: Frailty, as a proxy for biological age, was more strongly associated with pain management patterns than chronological age. Sole reliance on age may lead to undertreatment and ageist biases. These findings should, however, be interpreted with caution given the cross-sectional observational design, which precludes causal inference. Incorporating frailty into pain care strategies may nonetheless support more personalized, effective, and safer management across the adult lifespan.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".