Bibliographic record
Abstract
Purpose: To evaluate frailty and its relationship with geriatric syndromes in the context of socioeconomic variables. Patients and Methods: In this cross-sectional study, elderly people aged 65 years old and over who received homecare in the reference region of Crete, Greece, were enrolled. Geriatric syndromes such as frailty, dementia, and depression were evaluated using the SHARE-Frailty Index (SHARE-Fi), the Montreal Cognitive Assessment (MoCA), and the Geriatric Depression Scale (GDS), respectively. Level of education, annual individual income, disability in Activities of Daily Living (ADL) and homebound status were also assessed as ‘socioeconomic factors.’ Results: The mean age of 301 participants was 78.45 (±7.87) years old. A proportion of 38.5% was identified as frail. A multiple logistic regression model revealed that elderly people with cognitive dysfunction were more likely to be frail (OR = 1.65; 95% CI: 0.55−4.98, p = 0.469) compared to those with normal cognition, but this association was not significant. Although elderly people with mild depression were significantly more likely to be frail (OR = 2.62; CI: 1.33−5.17, p = 0.005) compared to those with normal depression, the association for elderly people with severe depression (OR = 2.05, CI: 0.80−5.24, p = 0.134) was not significant. Additionally, comorbidity (OR = 1.06, CI: 0.49−2.27, p = 0.876) was not associated with frailty, suggesting that comorbidity is not a risk factor for frailty. In addition, patients with mild depression were significantly more likely to report frailty (OR = 2.62, CI:1.33−5.17, p = 0.005) compared to those with normal depression, whereas elders with an annual individual income (>EUR 4500) were less likely to be frail (OR = 0.45, CI: 0.25−0.83, p = 0.011) compared to those with <EUR 4500 per year. Conclusions: Our data analysis shows that higher annual individual income and mild depression were independently associated with frailty, suggesting that a lower poverty threshold and mild depression are risk factors for frailty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 teacher head, 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".