High Prevalence of Falls Independent of Age in Adults Living With Chronic Pain
Bibliographic record
Abstract
OBJECTIVES: High risks of falls have been reported in older adults with chronic pain but chronic pain similarly affects adults of all ages. This cross-sectional study aimed to determine the prevalence of falls and associated risk factors in adults of all ages living with chronic pain. MATERIALS AND METHODS: Patient-reported data were analyzed from 591 adults with chronic pain enrolled in a local pain clinic between November 2017 and April 2019. Sociodemographic, history of falls, and biopsychosocial measures of pain were examined to identify and describe adults with chronic pain who fell in the previous year. Factors associated with falls were examined using logistic regression. RESULTS: A total of 268 (45%) reported at least 1 fall in the previous year (fallers) where 194 (33%) fell in the previous 3 months, and 185 (31%) had multiple falls. The prevalence of falls in the previous year was over 37% across age groups. Overall, fallers were older, had greater pain severity and interference, lower physical function and pain self-efficacy, greater depression, more reported neuropathic pain, and had more pain sites compared with nonfallers. Number of pain sites reported (odds ratio=1.12; 95% confidence interval, 1.02-1.22) and lower physical function (odds ratio=0.96; 95% confidence interval, 0.94-0.99) were independently associated with falls. DISCUSSION: A high prevalence of falls was found independent of age for adults with chronic pain. Although the risk of falls may increase with age, lower physical function and more pain sites are better indicators for falls. A better understanding of circumstances and consequences of falls in all adults with chronic pain is warranted.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".