The discriminant validity of single-question assessments of subjective cognitive complaints in an Asian older adult population
Bibliographic record
Abstract
Objective To compare the discriminant validity of three different single-question assessments of subjective cognitive complaints (SCC) for dementia in a community-based older adult population in Singapore. Methods Eligible older adults aged ≥60 were recruited into phase I for identifying those who require further assessment using the Abbreviated Mental Test (AMT) and progressive forgetfulness question (PFQ). Participants who failed either tests entered phase II and were administered various single-question assessments of SCC, such as the 8th question on the patient Ascertain Dementia 8 (AD8-8pt), informant AD8 (AD8-8info), and the 10th item on the Geriatric Depression Scale (GDS-10), followed by the Montreal Cognitive Assessment (MoCA) and a formal neuropsychological battery to identify the participant’s cognitive status by a research diagnosis and DSM-IV criteria. Differences in characteristics among diagnostic groups were compared. All discriminatory indices (sensitivity, specificity, positive, and negative predictive values, overall accuracy) for these single-question assessments and their combinations with the MoCA were calculated and reported to confirm their discriminant validity in identifying the existence of subjective complaints and objective impairment. Results A total of 3,780 participants were assessed at phase I, of which 957 entered and completed phase II. Of whom, 911 were dementia-free and 46 had dementia. The MoCA (13/14) displayed good sensitivity (95.6%), specificity (81.5%), and overall accuracy (82.1%) for dementia detection. The GDS-10 and AD8-8pt showed poor discriminant validity, while the AD8-8info had the highest specificity (83.2%) and the greatest overall accuracy (82.5%) for dementia. Compensatory combination of the AD8-8info with MoCA, the sensitivity and positive predictive values were optimized (100%), while the conjunctive combination of two tools achieved excellent specificity (96.3%) and overall accuracy (94.8%) in discriminating dementia patients. Conclusion and implications Combining a reliable single-question SCC assessment with an objective tool can efficiently discriminate dementia patients from healthy older adults in the community.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".