Stepwise identification of prodromal dementia: Testing a practical model for primary care
Bibliographic record
Abstract
BACKGROUND: Prodromal dementia is largely underdiagnosed in primary care. OBJECTIVE: To develop a clinical model for detecting prodromal dementia within the operative boundaries of primary care practice. METHODS: The study employed the Functional Activities Questionnaire (FAQ) and Montreal Cognitive Assessment (MoCA) to evaluate a "functional-cognitive" step-down screening model, in which the MoCA is administered subsequent to reported symptoms on the FAQ. It classified participants from the Alzheimer's Disease Imaging Initiative to three diagnostic categories: (1) healthy cognition (n = 396), (2) mild cognitive impairment without conversion (n = 430), and (3) prodromal dementia assessed 24 months before diagnosis (n = 164). RESULTS: Analyses indicated that the step-down model (Model 1) performed significantly better than an alternative model that applied the FAQ as a single measure (Model 2) and compared well with another model that administered both screening measures to all participants (Model 3). Gradient Boosting Trees classifications yielded the following estimations for Model 1/Model 2/ Model 3, respectively: Sensitivity = 0.87/0.77/0.89, Specificity = 0.68/0.47/0.70, PPV = 0.73/0.40/0.75, NVP = 0.84/0.81/0.87, F1 Score = 0.79/0.52/0.81, AUC = 0.78/0.67/0.79. CONCLUSIONS: These analyses support the proposed model. The study offers algorithms for validated measures, which were developed from a well characterized clinical sample. Their accuracy will likely improve further with new data from diverse clinical settings. These results can serve primary care in a timely manner in light of the recent advances in pharmacological treatment of dementia and the expected increase in demand for screening.
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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 | 0.049 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".