Examination of the Increased Risk for Falls Among Individuals With Knee Osteoarthritis: A Canadian Longitudinal Study on Aging <scp>Population‐Based</scp> Study
Bibliographic record
Abstract
OBJECTIVE: To characterize the profile of individuals with and without knee osteoarthritis (OA) who fell, and to identify factors contributing to an individual with knee OA experiencing 1 or multiple injurious falls. METHODS: Data are from the baseline and 3-year follow-up questionnaires of the Canadian Longitudinal Study on Aging, a population-based study of people ages 45-85 years at baseline. Analyses were limited to individuals either reporting knee OA or no arthritis at baseline (n = 21,710). Differences between falling patterns among those with and without knee OA were tested using chi-square tests and multivariable-adjusted logistic regression models. An ordinal logistic regression model examined predictors of experiencing 1 or more injurious falls among individuals with knee OA. RESULTS: Among individuals reporting knee OA, 10% reported 1 or more injurious falls; 6% reported 1 fall, and 4% reported 2+ falls. Having knee OA significantly contributed to the risk of falling (odds ratio [OR] 1.33 [95% confidence interval (95% CI) 1.14-1.56]), and individuals with knee OA were more likely to report having a fall indoors while standing or walking. Among individuals with knee OA, reporting a previous fall (OR 1.75 [95% CI 1.22-2.52]), previous fracture (OR 1.42 [95% CI 1.12-1.80]), and having urinary incontinence (OR 1.38 [95% CI 1.01-1.88]) were significant predictors of falling. CONCLUSION: Our findings support the idea that knee OA is an independent risk factor for falls. The circumstances in which falls occur differ from those for individuals without knee OA. The risk factors and environments that are associated with falling may provide opportunities for clinical intervention and fall prevention strategies.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".