Predictors of Severity of Cognitive Impairment in Rural Patients Presenting to a Memory Clinic (P01.089)
Bibliographic record
Abstract
Objective: To identify predictors of more severe cognitive impairment at initial presentation to a memory clinic in Saskatchewan. Background The literature suggests that patients with dementia and their families benefit from early assessment and diagnosis, yet those living in rural communities are disproportionately vulnerable to barriers in accessing dementia care. Design/Methods: Data collection began in 2004 at the Rural and Remote Memory Clinic in Saskatoon SK, where patients were referred by their family physicians. The questionnaires and assessments administered at the clinic-day appointment provided socio-demographic, clinical and functional variables. Socio-demographic variables included: age, sex, marital status, years of formal education, ancestry, number of people living with patient, number of comorbidities, time on clinic wait list, duration of symptoms, family history of dementia, and a measure of 9ruralness9. Caregiver-rated patient functional status was assessed by the Functional Activities Questionnaire (FAQ) and the Neuropsychiatric Inventory Scale. Caregiver burden was assessed through the Zarit Burden Interview; caregiver psychological distress through the Brief Symptom Inventory (BSI). The dependent variable was patient cognitive impairment, measured by Modified Mini-Mental State Examination (3MS) scores. Univariate and multiple linear regression analyses were conducted to determine predictors of cognitive impairment severity at clinic presentation. Results: Our sample included 198 patients (62% female). The mean age was 73.9 years (SD=9.2). We found that age and gender interaction, years of formal education, FAQ score, and BSI score were significantly associated with 3MS scores (p Conclusions: Increased cognitive impairment was predicted by fewer years of formal education, poorer functional ability, and less caregiver psychological distress. With respect to the age and gender interaction, younger females were more cognitively impaired than younger males at clinic day. In older patients, males were more cognitively impaired. Supported by: The Mach-Gaensslen Foundation. Disclosure: Dr. Lacny has nothing to disclose. Dr. Kirk has nothing to disclose. Dr. Morgan has nothing to disclose. Dr. Karunanayake has nothing to disclose.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".