Predicting the Risk of Dementia Among Canadian Seniors
Bibliographic record
Abstract
Research has not provided feasible models to identify dementia in primary care. We construct a broadly based diagnostic algorithm synthesizing information from known risk factors, such as poor cognition, sociodemographic factors, and health history. Data were from the Canadian Study of Health and Aging (CSHA) Phase I. Dementia was diagnosed by clinical consensus. All subjects had a Mini-Mental State Examination (MMSE) score and a Modified MMSE (3MS) score. Multiple logistic regression was used to build our diagnostic algorithm, which was then tested for classification accuracy on the basis of the area under the receiver operating characteristic curve. The area under receiver operating characteristic curve for our diagnostic algorithm using 3MS as a binary variable was significantly greater than the 3MS alone (P<0.001). However, no significant difference was found when using 3MS as a continuous variable in the algorithm. Similarly, a binary MMSE algorithm would provide greater accuracy than MMSE alone. In terms of the usage of our algorithm in practice settings, given the prevalence of dementia, the clear benefits of accurate identification and earlier intervention, adding a few questions to the binary 3MS in our algorithm quantitatively improves the dementia prediction, which is important for patients, caregivers, and health providers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".