Predictors of Cognitive Impairment Severity in Rural Patients at a Memory Clinic
Bibliographic record
Abstract
OBJECTIVE: Patients with dementia benefit from early assessment and diagnosis. In an attempt to identify factors leading to delay in referral, we investigated socio-demographic, clinical, and functional predictors of greater severity of cognitive impairment in dementia patients presenting to a memory clinic in Saskatoon, Saskatchewan. METHODS: Data collection began in 2004 at the Rural and Remote Memory Clinic in Saskatoon, where non-institutionalized patients were referred by their family physicians. The patient and caregiver questionnaires and assessments administered at the clinic day appointment provided the socio-demographic, clinical, and functional patient variables, as well as the caregiver stress and burden variables. The dependent variable was patient cognitive impairment, as measured by Modified Mini-Mental State Examination (3MS) scores. Variables underwent univariate linear regression with 3MS scores in order to determine possible associations. A multiple regression analysis was 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 an age and gender interaction, years of formal education, Functional Activities Questionnaire score, and Brief Symptom Inventory score were significantly associated with 3MS scores (p<0.05). CONCLUSIONS: Increased cognitive impairment at presentation was predicted by fewer years of formal education, poorer functional ability, and less caregiver psychological distress. There was a significant interaction between age and gender: younger females were more cognitively impaired than younger males at clinic day, while in older patients, males were more cognitively impaired than females.
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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 | 0.000 | 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) | 0.002 | 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".