Active case finding of dementia in ambulatory care settings: a comparison of three strategies
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To reduce the diagnostic gap of dementia, three strategies can be employed for case finding of cognitive impairment in ambulatory care settings, namely using informant report, brief cognitive test or a combination of informant report and brief cognitive test. The right strategy to adopt across different healthcare settings remains unclear. This diagnostic study compared the performance of the three strategies for detecting dementia (primary aim), as well as for detecting both mild cognitive impairment (MCI) and dementia (secondary aim). METHODS: Participants aged ≥65 years (n = 11 057) were recruited from Alzheimer's Disease Centers across the USA. Participants provided data on an informant report (Functional Activities Questionnaire), brief cognitive test (four-item short variant of Montreal Cognitive Assessment) and a combined measure with informant report and brief cognitive test (sum of Functional Activities Questionnaire and Montreal Cognitive Assessment short variant). They also received standardized assessments (clinical history, physical examination and neuropsychological testing) to diagnose MCI and dementia. Areas under the receiver operating characteristic curve (AUCs) of the three strategies were compared using the DeLong method, with AUC > 90% indicating excellent performance. RESULTS: All three strategies had excellent performance in detecting dementia, although informant report [AUC, 95.9%; 95% confidence intervals (CI), 95.4-96.3%] was significantly better than brief cognitive test (AUC, 93.0%; 95% CI, 92.4-93.6%) and the combined measure had the best performance (AUC, 97.0%; 95% CI, 96.7-97.4%). However, to detect both MCI and dementia, only the combined measure had excellent performance (AUC, 93.0%; 95% CI, 92.5-93.4%), whereas stand-alone informant report or brief cognitive test performed suboptimally (AUC < 90%). Performance of the three strategies was not affected by participants' age, educational attainment or underlying prevalence of MCI and dementia. CONCLUSIONS: For case finding of dementia in ambulatory care settings, informant reports would suffice as first-line measures and brief cognitive tests may optionally be added on, in services with available resources, to further improve the accuracy of detection. For case finding of both MCI and dementia, a combination of informant reports and brief cognitive tests remains the most appropriate strategy.
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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 | 0.027 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".