The reliability and validity of the informant AD8 by comparison with a series of cognitive assessment tools in primary healthcare
Bibliographic record
Abstract
BACKGROUND: The validity and reliability of the informant AD8 in primary healthcare has not been established. Therefore, the present study examined the validity and reliability of the informant AD8 in government subsidized primary healthcare centers in Singapore. METHODS: Eligible patients (≥60 years old) were recruited from primary healthcare centers and their informants received the AD8. Patient-informant dyads who agreed for further cognitive assessments received the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Clinical Dementia Rating (CDR), and a locally validated formal neuropsychological battery at a research center in a tertiary hospital. RESULTS: 1,082 informants completed AD8 assessment at two primary healthcare centers. Of these, 309 patients-informant dyads were further assessed, of whom 243 (78.6%) were CDR = 0; 22 (7.1%) were CDR = 0.5; and 44 (14.2%) were CDR≥1. The mean administration time of the informant AD8 was 2.3 ± 1.0 minutes. The informant AD8 demonstrated good internal consistency (Cronbach's α = 0.85); inter-rater reliability (Intraclass Correlation Coefficient (ICC) = 0.85); and test-retest reliability (weighted κ = 0.80). Concurrent validity, as measured by the correlation between total AD8 scores and CDR global (R = 0.65, p < 0.001), CDR sum of boxes (R = 0.60, p < 0.001), MMSE (R = -0.39, p < 0.001), MoCA (R = -0.41, p < 0.001), as well as the formal neuropsychological battery (R = -0.46, p < 0.001), was good and consistent with previous studies. Construct validity, as measured by convergent validity (R ≥ 0.4) between individual items of AD8 with CDR and neuropsychological domains was acceptable. CONCLUSIONS: The informant AD8 demonstrated good concurrent and construct validity and is a reliable measure to detect cognitive dysfunction in primary healthcare.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".