Frequency of Going Out and Locomotive Syndrome Among Japanese Female Elderlies
Bibliographic record
Abstract
Background: Japan is the world’s leading super-aged society, which makes locomotive syndrome an urgent issue. Because increasing the frequency of going out is considered a practical primary preventive measure against locomotive syndrome, we examined the relationship between the frequency of going out and locomotive syndrome in elderly females in Japan. Methods: The subjects were 8,027 females from 46 prefectures in Japan who were living at home and aged 65 and older as of November 1, 2012. The study period was from November 1 to December 31, 2012. The survey was implemented by distributing questionnaires, as well as conducting face–to–face interviews. Odds ratios were obtained using logistic regression models with locomotive syndrome as the dependent variable. Results: Eight thousands twenty seven females were analyzed in this study. There was a significant difference in the prevalence of locomotive syndrome depending on the frequency of going out (p<0.001) as the prevalence of locomotive syndrome decreased as the frequency of going out increased. When the results were adjusted for gender, the frequency of going out, age, use of national nursing–care insurance services, household composition, severity of obesity, and self-rated health, the prevalence of locomotive syndrome was high in those whose frequency of going out was “twice or less a week” (Odds ratio: 1.41, 95% Confidence interval 1.20–1.64). Conclusions: The results suggest that it is possible to prevent locomotive syndrome by encouraging elderly people to maintain and increase their frequency of going out.
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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 | 0.001 |
| 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".