Síndrome da fragilidade e riscos para quedas em idosos da comunidade
Bibliographic record
Abstract
PURPOSE: To identify the prevalence of Frailty Syndrome in the elderly and the relationship with risk of falling. METHODS: Descriptive, cross-sectional, and analytical clinical study. One hundred and one volunteers over 60 years old were submitted to audiological evaluation, Dynamic Gait Index - Brazilian brief (DGI), Timed Up and Go (TUG) and Edmonton Fragility Scale (EFE) that verified, respectively, hearing thresholds, frailty syndrome, functional and dynamic balance, and risk of falling. The simple percentual distribution, the Wilcoxon´s test and the Bivariate Correlation with Pearson's coefficient were used for statistical analysis. Limits equal to or less than 1.0 and 5.0% were adopted. RESULTS: EFE identified 22.8% of volunteers as fragile and 22.8% as vulnerable. DGI and TUG found 34.6% and 84.1% of at risk for falls, respectively. Significant correlations between EFE and DGI (p <0.01), EFE and TUG (p <0.01), and DGI and TUG (p <0.01) were observed. Pearson's coefficient between EFE and DGI, EFE and TUG, and DGI and TUG were -0.26, -0.41, and 0.46, respectively. An association between DGI and TUG and age (p <0.01) was identified. No correlation between EFE and sex or age was found. CONCLUSION: Frailty and pre-frailty were identified in a significant segment of the volunteers, especially in the oldest subjects. Functional and dynamic balance were moderately correlated with frailty, which demonstrated that frailty syndrome increases the risk of falls.
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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 | 0.003 |
| 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".