ArticleGeriatrics & gerontology international2024
Identifying factors associated with locomotive syndrome using machine learning methods: The third survey of the research on osteoarthritis/osteoporosis against disability study.
Article in Geriatrics & gerontology international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Identifying factors associated with locomotive syndrome using machine learning methods: The third survey of the research on osteoarthritis/osteoporosis against disability study.Geriatrics & gerontology international · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
aimTo identify factors associated with locomotive syndrome (LS) using medical questionnaire data and machine learning.
methodsA total of 1575 participants underwent the LS risk tests from the third survey of the research on osteoarthritis/osteoporosis against disability study (ROAD) study. LS was defined as stage 1 or higher based on clinical decision limits of the Japanese Orthopaedic Association. A total of 1335 items of medical questionnaire data came from this study. The number of medical questionnaire items was reduced from 1335 to 331 in data cleaning. From the 331 items, identify factors associated with LS use by light gradient boosting machine-based recursive feature elimination with cross-validation. The performance of each set was evaluated using an average of seven performance metrics, including 95% confidence intervals, using a bootstrapping method. The smallest set of items is determined with the highest average of receiver operating characteristic area under the curve (ROC-AUC) under 20 items as association factors of LS. Additionally, the performance of the selected items was compared with the LS risk tests and Loco-check.
resultsThe nine items have the best average ROC-AUC under 20 items. The nine items show an average ROC-AUC of 0.858 (95% confidence interval 0.816-0.898). Age and back pain during walking were strongly associated with the prevalence of LS. The ROC-AUC of nine items is higher than that of existing questionnaire-based LS assessments, including the 25-question Geriatric Locomotor Scale and Loco-check.
conclusionsThe identified nine items could aid early LS detection, enhancing understanding and prevention. Geriatr Gerontol Int 2024; 24: 806-813.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.