Physical frailty and health outcomes of fitness, hormones, psychological and disability in institutionalized older women: an exploratory association study
Bibliographic record
Abstract
Little is known about symptoms associated with frailty in institutionalized Portuguese older adults. This study aimed to investigate the association of frailty with diverse geriatric health characteristics. Cross-sectional data from 140 women aged between 75 and 85 years were analyzed. Data were collected between March and June, 2016. Fried’s definition of physical frailty, psychological, sex hormones, disability and physical fitness outcomes were examined. The prevalence of frailty was 40%. Frail women had lower scores in cognitive and physical fitness, and higher scores for depressive symptoms and comorbidities. Significant correlations emerged between frailty and disability, fear of falling, aerobic resistance and cognition. Regression analyses and Receiver Operating Only aerobic resistance (sensitivity [93–96%]; specificity [74–77%], p = .001) and cognition (sensitivity [77–88%]; specificity [65–71%], p < .001) remained in the equation as independently related to physical frailty. A trend of significant differences in lower systolic blood pressure may reflect being less physically active and/or having more systemic comorbidity. Fried’s model can be considered applicable. The 2-minute step test and the Mini Mental State Examination could better identify frail populations. The role of blood pressure and level of education in physical frailty status needs to be further explored.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".