Less Education Predicts Anticholinesterase Discontinuation in Dementia Patients
Bibliographic record
Abstract
OBJECTIVE: We investigated patient socio-demographic, clinical and functional factors predicting cholinesterase inhibitor discontinuation by patients presenting to a memory clinic in Saskatoon, Saskatchewan. METHODS: Data collection began in March 2004 at the Rural and Remote Memory Clinic where family physicians referred their non-institutionalized patients. Neurological and neuropsychological assessment, patient and caregiver questionnaires provided the socio-demographic, clinical and functional variables. Univariate logistic regression analysis was used to examine possible associations between each independent variable and the binary outcome variable of treatment discontinuation. Multivariate logistic regression was used to determine predictors of cholinesterase inhibitor discontinuation within six months of drug initiation. RESULTS: Our sample consisted of the first 63 patients (60.3% female) for whom we prescribed a cholinesterase inhibitor. The mean age at clinic day was 74.56 years (SD=7.78). We found that years of formal education was the only variable significantly associated with cholinesterase inhibitor discontinuation by six months. The more years of formal education, the lower the rate of drug discontinuation by six months. CONCLUSIONS: Likelihood of cholinesterase inhibitor discontinuation by six months was predicted by fewer years of formal education.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".