The association of painful and non-painful morbidities with frailty: a cross sectional analysis of a cohort of community dwelling older people in England
Bibliographic record
Abstract
INTRODUCTION: The association between chronic pain and frailty might indicate that pain is an independent driver of frailty but might alternatively be explained by inclusion within frailty identification tools of morbidities that commonly lead to chronic pain. This research examines the extent to which the association of pain with frailty might be attributed to morbidities. METHODS: A cross-sectional analysis of older people in a UK cohort with or at risk of musculoskeletal problems or frailty (Investigating Musculoskeletal Health and Wellbeing study), used multivariable logistic regression and Z-tests to assess the degrees of associations of pain (McGill Pain Rating Index), and painful and non-painful morbidity counts with frailty (modified FRAIL questionnaire). RESULTS: Data were from 2,185 participants, 56% female, median age 73 (range 60 to 96) years. 430 (20%) participants were classified as frail. In a fully adjusted standardised model, pain (aOR 2.07 (95%CI 1.83 to 2.33) and 'any' morbidity aOR (1.74 (95%CI 1.54 to 1.97) were both significantly associated with frailty. When morbidity was subclassified as painful or non-painful, painful (aOR 1.48 (95%CI 1.30 to 1.68) and non-painful (aOR1.39 (95%CI 1.24 to 1.56)) morbidities each were associated with frailty, as also was pain (aOR 2.07 (95%CI 1.83 to 2.34, p < 0.001). CONCLUSIONS: Pain is associated with frailty, over and above any effect of painful and non-painful morbidities. This forms the justification for future research which focuses on pain management in the identification, prevention, and treatment of frailty.
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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 | 0.001 |
| 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".