An abbreviated version of Silhouettes test: a brief validated mild cognitive impairment screening tool
Bibliographic record
Abstract
OBJECTIVES: To revise an abbreviated version of the Silhouettes subtest of the Visual Object and Space Perception (VOSP) battery in order to recognize mild cognitive impairment (MCI) and determine the optimal cutoffs to differentiate among cognitively normal controls (NC), MCI, and Alzheimer's Disease (AD) in the Chinese elderly. DESIGN: A cross-sectional validation study. SETTING: Huashan Hospital, Shanghai, China. SUBJECTS: A total of 591 participants: Individuals with MCI (n = 211), AD (n = 139) and NC (n = 241) were recruited from the Memory Clinic, Huashan Hospital, Shanghai, China. METHODS: Baseline neuropsychological battery (including VOSP) scores were collected from firsthand data. An abbreviated version of silhouettes test (Silhouettes-A) was revised from the original English version more suitable for the elderly, including eight silhouettes of animals and seven silhouettes of inanimate objects, with a score ranging from 0 to 15. RESULTS: Silhouettes-A was an effective test to screen MCI in the Chinese elderly with good sensitivity and specificity, similar to the Montreal cognitive assessment and superior to other single tests reflecting language, spatial, or executive function. However, it had no advantage in distinguishing MCI from AD. The corresponding optimal cutoff scores of Silhouettes-A were 10 for screening MCI and 8 for AD. CONCLUSION: Silhouettes-A is a quick, simple, sensitive, and dependable cognitive test to distinguish among NC, MCI, and AD patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".