Clinical Utility of the Informant AD8 as a Dementia Case Finding Instrument in Primary Healthcare
Bibliographic record
Abstract
The informant AD8 has excellent discriminant ability for dementia case finding in tertiary healthcare settings. However, its clinical utility for dementia case finding at the forefront of dementia management, primary healthcare, is unknown. Therefore, we recruited participants from two primary healthcare centers in Singapore and measured their performance on the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Clinical Dementia Rating (CDR), and a local formal neuropsychological battery, in addition to the AD8. Logistic regression was conducted to examine the associations between demographic factors and dementia. Area under the receiver operating characteristics (ROC) curve analysis was used to establish the optimal cut-off points for dementia case finding. Of the 309 participants recruited, 243 (78.7%) had CDR = 0, 22 (7.1%) CDR = 0.5, and 44 (14.2%) CDR ≥1. Age was strongly associated with dementia, and the optimal age for dementia case finding in primary healthcare settings was ≥75 years. In this age group, the AD8 has excellent dementia case finding capability and was superior to the MMSE and equivalent to the MoCA [AD8 AUC (95% CI): 0.95 (0.91-0.99), cut-off: ≥3, sensitivity: 0.90, specificity: 0.88, PPV: 0.79 and NPV: 0.94; MMSE AUC (95% CI): 0.87 (0.79-0.94), p = 0.04; MoCA AUC (95% CI): 0.88 (0.82-0.95), p = 0.06]. In conclusion, the AD8 is well suited for dementia case finding in primary healthcare settings.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".