Predicting cognitive impairment among geriatric patients at Asir central hospital, Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is an aging-related disease that can result in a variety of health problems as disability and death. Cognitive impairment was reported to be more than 40% among elderly individuals. AIM: To predict cognitive impairment among geriatric patients at Asir central hospital, Saudi Arabia and its relationship to health status. STUDY DESIGN: A descriptive correlational study design was used to conduct this study. The study included a convenient sample of all geriatric patients (130) attending outpatient clinics of Asir central hospital in Abha city from the first of February to the mid of March 2020. Three tools were utilized to collect data pertinent to this study; Tool (I): structured geriatric patient's sociodemographic and clinical data interview questionnaire, Tool II: Geriatric Depression Scale Short-Form (GDS-SF) and Tool III: Mini Mental State Examination (MMSE) Scale. RESULTS: The majority of the studied sample was in the age group between 60 and 69 years, and female, having a chronic disease, (31.6%) were having a mild cognitive impairment and (17%) were having severe cognitive impairment and there was an association between cognitive impairment levels and health status of the studied patients with no statistically significant difference. CONCLUSION: Nearly one third were having a mild cognitive impairment and about one fifth were having severe cognitive impairment. There was a correlation between levels of cognitive impairment and health status of the studied patients. RECOMMENDATIONS: Health education programs to increase the awareness of the Saudi community about cognitive impairment and its risk factors are needed. Elderly cognitive screening services must be readily available for early diagnosis and early treatment of cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".