Detection of Cognitive Impairment and Dementia Using the Animal Fluency Test: The DECIDE Study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the performance of a one-minute screening test measured against a validated 10-minute screening test for mild cognitive impairment (MCI) in detecting CI in patients aged > or = 65 years with two or more vascular risk factors (VRF). METHODS: Patients (n=1523) aged 65 years or older without documented CI symptoms or dementia with two or more VRF participated in this study set in Canadian primary care practice. Baseline data was collected, followed by the 1-minute animal fluency (AF) test and the 10-minute Montreal Cognitive Assessment (MoCA). Physicians (n=122) completed case reports during patient interviews and reported their diagnostic impression. AF test sensitivity, specificity, and accuracy in predicting a positive MoCA was assessed. RESULTS: Study sample mean age was 79.7 years, 55% were female, 97.6% were Caucasian and 75% had < or = 12 years of education. The AF test and MoCA detected CI in 52 and 56 percent of the study population, respectively. The AF test demonstrated sensitivity, specificity, and accuracy in predicting a positive MoCA of 67 percent each. Physicians diagnostic impression of MCI was reported for 37% of patients, and of dementia for 6%. CONCLUSION: In an elderly population with at least two VRF, using AF can be useful in detecting previously unknown symptoms of CI or dementia. Screening for CI in this high risk population is warranted to assist physician recognition of early CI. The short AF administration time favours its incorporation into clinical practice.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".