Discriminant Validity and Time‐effectiveness of A Stepwise Dementia Case‐finding Approach in an Asian Elderly Community
Bibliographic record
Abstract
Abstract Background To investigate the discriminant validity and time‐effectiveness of a stepwise dementia case‐finding approach in a community‐based Singaporean older adult population. Method Participants who completed the Progressive Forgetfulness Question (PFQ) and the Abbreviated Mental Test (AMT) were invited to phase II and administered the Mini‐Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and a formal neuropsychological battery. Participants were diagnosed by cognitive performance and DSM‐IV criteria into No Cognitive Impairment (NCI), Cognitive Impairment‐No Dementia (CIND) mild (≤2 cognitive domains impaired), CIND moderate (>2 domains impaired) and dementia. Receiver Operating Characteristic were conducted for different cognitive instruments (AMT, MoCA, MMSE) between PFQ = Yes and PFQ = No groups. All discriminant indices including sensitivity, specificity, positive (PPV), negative predictive values (NPV) and accuracy were calculated. Bayesian correction methods were used to adjust the verification bias. Screening time saving and number of subjects further evaluated was estimated in two scenarios with or without the PFQ. Result The PFQ showed an NPV of 94.2% for excluding dementia‐free cases after adjustment. After excluding PFQ = No participants, all cognitive tools achieved optimal NPV (>99%). Furthermore, the number of people requiring further evaluation decreased by 129 (42.7%), 58 (36.5%), 25 (14.3%) subjects, corresponding to 48.2%, 48.2%, and 47.8% of screening time being saved, when the PFQ was conducted prior to the MoCA, MMSE, and AMT, respectively. Conclusion Using a single‐question assessment as the first step of a case‐finding approach, followed up a cognitive test such as the MoCA, could minimize time and resources for further investigation 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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".