Evaluation of Frailty Elderly Syndrome, General Health and Cognitive Disorders in the Elderly and Their Relationship with Demographic Factors and Underlying Diseases
Bibliographic record
Abstract
Introduction: Due to the vulnerability of the elderly in the community and the increase in their population and the problems occurring due to aging in the elderly, the present study aimed to investigate the frailty elderly syndrome, general health and cognitive disorders in the elderly in Ilam in 2020. Materials and Methods: This cross-sectional study was performed on 267 elderly referring to teaching hospitals in Ilam. Data collection tools were GHQ-28, MMSE and Edmonton questionnaires. Data were analyzed using SPSS v.24 statistical software using independent t-test, chi-square, Fisher’s exact test, one-way and two-way analysis of variance, Mann-Whitney, Kruskal-Wallis and linear regression. Results: The mean ± standard deviation of the overall score of general health, frailty elderly syndrome and cognitive disorders in the studied elderly were 44.38±13.43, 9.29±2.98 and 23.40±6.49, respectively. There was a statistically significant relationship between cognitive disorders with age, marital status, education and underlying diseases and also between frailty elderly syndrome with age, marital status, place of residence, gender, education, type of insurance and some underlying diseases (P <0.001). There was a significant relationship between general health of the elderly and pulmonary disease (P = 0.03). Conclusion: The general health of the elderly was low so that most of the elderly suffered from mild symptoms, the greatest effect was related to depression and then anxiety (psychological dimension). Regarding cognitive disorders, a large number of patients did not have any pathological disorders. The rate of frailty elderly syndrome was high in the study population. Keywords: frailty elderly syndrome, Cognitive disorders, General health
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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) | 0.001 | 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".