Clinical osteoarthritis of the hip and knee and fall risk: The role of low physical functioning and pain medication
Bibliographic record
Abstract
OBJECTIVE: Several studies have found an increased fall risk in persons with osteoarthritis (OA). However, most prospective studies did not use a clinical definition of OA. In addition, it is not clear which factors explain this risk. Our objectives were: (1) to confirm the prospective association between clinical OA of the hip and knee and falls; (2) to examine the modifying effect of sex; and (3) to examine whether low physical performance, low physical activity and use of pain medication are mediating these relationships. METHODS: Baseline and 1-year follow-up data from the European Project on OSteoArthritis (EPOSA) were used involving pre-harmonized data from five European population-based cohort studies (ages 65-85, n = 2535). Clinical OA was defined according to American College of Rheumatology (ACR) criteria. Falls were assessed using self-report. RESULTS: Over the follow-up period, 27.7% of the participants fell once or more (defined as faller), and 9.8% fell twice or more (recurrent faller). After adjustment for confounding, clinical knee OA was associated with the risk of becoming a recurrent faller (relative risk=1.55; 95% confidence interval: 1.10-2.18), but not with the risk of becoming a faller. No associations between clinical hip OA and (recurrent) falls were observed after adjustment for confounding. Use of opioids and analgesics mediated the associations between clinical OA and (recurrent) falls, while physical performance and physical activity did not. CONCLUSION: Individuals with clinical knee OA were at increased risk for recurrent falls. This relationship was mediated by pain medication, particularly opioids. The fall risk needs to be considered when discussing the risk benefit ratio of prescribing these medications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".