Impact of socioeconomic status on initial clinical presentation to a memory disorders clinic
Bibliographic record
Abstract
BACKGROUND: Dementia affects 15% of Canadians 65 and older, and the prevalence is expected to double over the next two decades. Low socioeconomic status (SES) can increase the risk of Alzheimer's disease (AD) and the precursor mild cognitive impairment (MCI), but it is unknown what the relationship of SES is on initial clinical presentation to a memory disorders clinic. METHODS: Data from 127 AD and 135 MCI patients who presented to our Memory Disorders Clinic from 2004 to 2013 were analyzed retrospectively. We examined the relationship between SES (measured using Hollingshead two-factor index) and (1) diagnosis of either AD or MCI; (2) age when first presented to clinic; (3) objective cognitive tests to indicate clinical severity; and (4) the use of cognitive enhancers, medication for treating mild-to-moderate AD patients. RESULTS: AD patients had lower SES than MCI patients (p < 0.001, r = 0.232). Lower SES was associated with a greater age at initial time of diagnosis (χ2 = 11.5, p = 0.001). In MCI patients, higher SES individuals outperformed lower SES individuals on the BNA after correcting for the effect of age (p = 0.004). Lower SES was also associated with decreased use of cognitive enhancers in AD patients (p < 0.001, r = 0.842). CONCLUSION: Individuals with lower SES come into memory clinic later when the disease has progressed to dementia, while higher SES individuals present earlier when the disease is still in its MCI stage. There were more higher SES individuals who presented to our memory clinic. Higher SES is associated with better cognitive functioning and increased use of cognitive enhancers. The health policy implication is that we need to better engage economically disadvantaged individuals, perhaps at the primary care level.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".