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.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".