POS1005-HPR RELIABILITY AND VALIDITY OF THE CONE EVASION WALK TEST IN KNEE OSTEOARTHRITIS
Bibliographic record
Abstract
Background Knee osteoarthritis (OA) can cause a variety of dysfunctions leading to limitations in mobility, gait, and balance that predisposes them to increased fall risk [1,2]. Falls are the leading cause of injury and fracture [3], putting a significant financial burden on the healthcare system. The fall risk is even higher in people with knee OA with a prevalence between 23 and 63% [4]. Another study reported that almost 50% of patients with knee OA experienced falls [5]. Therefore, the identification of fall predictors is essential to minimize fall incidence [6]. The Cone Evasion Walk Test (CEW) evaluates fall risk by the ability to evade obstacles and walking, which provides a versatile assessment including attentional, perceptual, seeing, and several neuromusculoskeletal and movement-related functions and can be performed with or without a walking aid [7]. Objectives The study aimed to investigate the reliability, validity, and minimal clinically important difference (MCID) of the CEW in people with knee OA. Methods Thirty-three patients with knee OA were included. Patients performed trials for the CEW and the Timed up and Go Test on the same day. Between the trials, patients waited for an hour in a sitting position to prevent fatigue. Results The CEW was shown to have excellent test-retest reliability and moderate validity (p<0.001). The relative (ICC coefficient) and absolute (SEM and SRD95) reliability of the CEW were 0.97, 0.73, and 2.02 respectively. The Pearson correlation coefficient between the CEW and the TUG was 0.72. Conclusion The measurements support the use of the CEW to evaluate dynamic balance and obstacle avoidance of knee OA patients. The analysis demonstrated excellent reliability and moderate validity. The low MCID value (2.02) indicated that it is a responsive test to identicate small changes in a patient’s status. The CEW can be used for a global evaluation of the function and mobility of knee OA patients with little space and equipment, easily and quickly. References [1]Khalaj N, Abu Osman NA, Mokhtar AH, Mehdikhani M, Wan Abas WAB. Balance and Risk of Fall in Individuals with Bilateral Mild and Moderate Knee Osteoarthritis. PLoS One. 2014 Mar 18;9(3):e92270. [2]Arnold CM, Gyurcsik NC. Risk Factors for Falls in Older Adults with Lower Extremity Arthritis: A Conceptual Framework of Current Knowledge and Future Directions. Physiotherapy Canada. 2012 Jul;64(3):302–14. [3]Cai G, Li X, Zhang Y, Wang Y, Ma Y, Xu S, et al. Knee symptom but not radiographic knee osteoarthritis increases the risk of falls and fractures: results from the Osteoarthritis Initiative. Osteoarthritis Cartilage. 2022 Mar 1;30(3):436–42. [4]Blasco JM, Pérez-Maletzki J, Díaz-Díaz B, Silvestre-Muñoz A, Martínez-Garrido I, Roig-Casasús S. Fall classification, incidence and circumstances in patients undergoing total knee replacement. Sci Rep [Internet]. 2022 Dec 1 [cited 2023 Jan 14];12(1):19839. Available from:/pmc/articles/PMC9674575/ [5]Thompson DP, Moula K, Woby SR. Are fear of movement, self-efficacy beliefs and fear of falling associated with levels of disability in people with osteoarthritis of the knee? A cross sectional study. Musculoskeletal Care [Internet]. 2017 Sep 1 [cited 2023 Jan 14];15(3):257–62. Available from: https://pubmed.ncbi.nlm.nih.gov/27925419/ [6]Rosadi R, Jankaew A, Wu PT, Kuo LC, Lin CF. Factors associated with falls in patients with knee osteoarthritis: A cross-sectional study. Medicine [Internet]. 2022 Dec 12 [cited 2023 Jan 14];101(48):e32146. Available from:/pmc/articles/PMC9726291/ [7]Sjöholm H, Hägg S, Nyberg L, Rolander B, Kammerlind AS. The Cone Evasion Walk test: Reliability and validity in acute stroke. Physiotherapy Research International. 2019 Jan;24(1):e1744. Acknowledgements: NIL. Disclosure of Interests None Declared.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".