Association between socio-demographic profile and severity of cognitive impairment in elder patients presenting with new onset of psychiatric symptoms: a cross sectional study
Bibliographic record
Abstract
Background: Cognitive impairment in the elderly is a common condition and, in most instances, primary care providers are the first point of contact for a patient and family. This study was aimed to find out the association between socio-demographic profile and severity of cognitive impairment in elder patients presenting with new onset of psychiatric symptoms.Methods: A cross sectional study was done among elder subjects (≥60 years of age) presented with new onset of psychiatric symptoms during one year period. A structured questionnaire was used to assess the socio-demographic details such as age, sex, education, occupation, socio-economic status and marital status. Mini International Neuropsychiatric interview and Montreal Cognitive Assessment scale were used for psychiatric diagnosis and severity of cognitive impairment grading, respectively. Association between socio-demographic data and cognitive impairment was statistically analyzed.Results: Among the 67 subjects included in the study, 76.2% had cognitive impairment. Majority of the subjects were females (56.7%) in the age group of 66-70 years. The association between cognitive impairment and sex (p=0.006), education (p=0.002) and occupation (p=0.015) were significant. But no significant association between cognitive impairment and marital status (p=0.0137) or socioeconomic status (p=0.400) was evidenced.Conclusions: Females in the age group of 66-70 years were more prevalent to cognitive impairment. The association between sex, education, occupation and severity of cognitive impairment was significant. No significant association between severity of cognitive impairment score and marital status or socioeconomic status was found.
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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 | 0.001 |
| 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".