Impact of socioeconomic status on the prevalence of dementia in an inner city memory disorders clinic
Bibliographic record
Abstract
BACKGROUND: Socioeconomic status (SES) has been identified as a possible risk factor for the development of dementia, with low SES shown to be associated with a higher prevalence of dementia, increased psychiatric comorbidity and worse baseline cognitive functioning. Few studies have actually looked at the impact of SES within a clinical population using multiple measures of SES and cognition. METHODS: Data on 217 patients seen in an Inner City Memory Disorders Clinic were analyzed with respect to demographic status, clinical status and SES. Correlations were then examined looking at the relationship of SES to clinical variables and neurocognitive status. Regression analysis was undertaken to examine the relative contribution of individual sociodemographic factors to a diagnosis of dementia. RESULTS: In general, there was wide variation in the sample examined with respect to most measures of SES. Approximately one third (36%) of the sample had a diagnosis of dementia, the mean age was 66.1 years and the mean Mini-mental State Examination score was relatively high (25.4). There was a strong association between age, individual annual income range, education, medical comorbidity and a diagnosis of dementia, with increased age and medical comorbidity being the strongest predictors. CONCLUSION: Increased age, low education, high medical comorbidity and low annual income are all associated with a diagnosis of dementia in an inner city setting. Age and medical comorbidity appear to be more strongly associated with a diagnosis of dementia than SES in an inner city setting.
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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 | 0.000 | 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) | 0.003 | 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".