Demographic Determinants of the Cognitive Status among Older Adults: Neyshabur Longitudinal Study on Aging
Bibliographic record
Abstract
Introduction: Cognitive disorders affect the elderly’s personal and social life by causing disturbances in their nervous system, and it is important to identify who is at the highest risk of these disorders; therefore, the present study aims to investigate demographic determinants of the cognitive status in the elderly visiting the Geriatric Cohort Center in Neyshabur. Methods: This cross-sectional study was conducted on 3451 people aged 60 and above (52.5% women and 47.5% men) from 2015 to the end of 2017. The selection of statistical sample was done first by classification method, and then, by random method. Data collection was done using demographic questionnaires, and the questionnaires of Mini Mental State Examination (MMSE)and Montreal Cognitive Assessment (MOCA), and data were analyzed using independent t-test, ANOVA, and hierarchical multiple regression in software. were analyzed by SPSS16 software. Results: With MMSE, one fifth of the participants (20/3%) and with MOCA, almost half of the respondents (49/3%) did not have cognitive disorder. Cognitive disorder became more severe with aging. The severity of cognitive disorder was higher in women (P< 0/05) those who were living alone(P <0/05), the women who only did housework(P < 0/05), and the cases who were illiterate (P< 0/05). Conclusion: Screening of cognitive disorders in the elderly and early interventions including holding educational classes, especially for women with low literacy levels whose husbands have died, can prevent the progression of the disorder and improve their quality of life.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".