Cognitive screening of older adults: the utility of pentagon drawing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Drawing tests have a long history in neuropsychological assessment. A popular geometric figure has been the two intersecting pentagons from the Bender Gestalt test. Reproducing the pentagons is the main visuospatial task on the original Mini-Mental State Examination (MMSE), remaining in use in revised versions of that widely used screening test. Scoring criteria on the MMSE are binary: perfect reproduction of the figure is required, while the Modified MMSE of Teng and Chui (1987) uses a more refined ten-point scoring for the elements of the figure. METHODS: Here, I report on the use of pentagon drawing from 8,702 older community-dwelling Canadians (59.3% female), with a mean age of 75.5 years (SD = 6.99) and 10.1 years of education (SD = 3.89). Mean scores for the whole sample are reported, as well as for subsamples who underwent a full clinical assessment and were diagnosed as cognitively intact, with dementia, or cognitively impaired, but without dementia. Logistic regression was used to evaluate the utility of pentagon drawing as a diagnostic tool to diagnose cognitive impairment. RESULTS: Binary scoring was less effective in discriminating groups than the ten-point system and showed weaker properties by other criteria. CONCLUSIONS: The discussion focuses on the role of simple, non-verbal tasks in the cognitive screening of older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it