Determining the association of medical co-morbidity with subjective and objective cognitive performance in an inner city memory disorders clinic: a retrospective chart review
Bibliographic record
Abstract
BACKGROUND: Medical co-morbidity may be associated with impaired cognitive function based on prior studies. However, no studies to date have determined to what extent this association is linked to medical illness or other factors that may be linked to medical illness (such as education, income levels, depression or subjective memory loss). The present study examined how medical co-morbidity, socioeconomic status (defined as residential SES), education and depression are associated with subjective and objective memory function in a sample of patients recruited from a university affiliated Memory Disorders Clinic located in a large Canadian inner city teaching hospital. METHODS: Data was collected from 85 consecutive referrals to an Inner City Memory Disorders Clinic including socio-demographic characteristics, cognitive status and medical co-morbidity. Descriptive and correlational analyses were conducted. RESULTS: Impaired objective cognitive function correlated significantly with increased medical co-morbidity and partially with education but not with residential SES or depression. Elevated memory complaints correlated significantly with depression, inversely with residential SES and not at all with medical co-morbidity or education. CONCLUSIONS: Increased medical co-morbidity is significantly associated with impaired cognitive performance but not with subjective memory complaints in an Inner City Memory Clinic sample.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".