FRAILTY SYNDROME IN OLDER ADULTS WITH AND WITHOUT FEAR OF FALLING
Bibliographic record
Abstract
The aim of this study was to determine the presence of frailty syndrome between elders with and without a fear of falling (FoF). A cross-sectional study was conducted with 203 older adults, divided into two groups: with FoF (n=91) and without FoF (n=112). The FoF was assessed using the yes/no question, “Are you afraid of falling?”. Frailty syndrome was evaluated by the Edmonton Frail Scale (EFE), which has a maximum score of 17 points, representing the highest degree of frailty. The scores for the frailty analysis are: no frailty (0–4), visibly vulnerable (5–6), mild frailty (7–8), moderate frailty (9–10), and severe frailty (11 or over). The groups did not differ in age, gender or general health conditions. The Mann-Whitney test showed a significant difference in EFE scores (p<.001) between the groups without FoF (mean:3.5; SD:1.94; 95%CI: 3.16–3.95; minimum-maximum:0–8 points) and with FoF (mean:4.88; SD:2.36; 95%CI: 4.32–5.43; minimum-maximum:0–12 points). The chi-square test showed that most of the elderly group without FoF was considered not frail (n=72; 64.3%, p<.001), 30 were visibly vulnerable (26.8%, p<.001) and only 10 were classified as mildly frail (8.9%, p<.001). Of the group with FoF, 46.2% (n = 42, p<.001) were considered not frail, 19 were visibly vulnerable (20.9%, p<.001), and 30 were frail (32.9%, p<.001). Of the older adults considered frail, 17 were mildly frail, seven were moderately frail, and six were severely frail. Thus, we conclude that older people with FoF have increased frequency and intensity of frailty syndrome than the elderly without FoF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".