A - 182 Comparison of the Short Test of Mental Status to Traditional Neuropsychological Tests
Bibliographic record
Abstract
Abstract Objective The Short Test of Mental Status (STMS) was developed by Kokmen and colleagues as a brief cognitive screening test. Existing research has found the STMS to be sensitive in detecting cognitive impairment in patients with Mild Cognitive Impairment (MCI). The STMS has also been shown to be comparable to other cognitive screening instruments, including the Montreal Cognitive Assessment (MoCA). However, the relationship between the STMS and traditional neuropsychological tests has yet to be evaluated. This study sought to explore the concordance between the STMS test items and neuropsychological test variables in a mixed clinical sample. Method Neuropsychological test data was collected from an archival database for 150 adults (Mean age = 71.4 years) referred to an interdisciplinary dementia care clinic at an academic medical center. Descriptive statistics for the demographic and test variables were produced. Pearson correlations were used to examine the relationships between the STMS total score, STMS item scores, and the neuropsychological test variables. Results Statistically significant correlations were found between the STMS item scores and their corresponding neuropsychological tests: Orientation (r = 0.46–0.56), Attention (r = 0.39–0.51), Registration-Trials (r = 0.32–0.50), Calculations (r = 0.30–0.41), Construction (r = 0.43), Information (r = 0.56), and Recall (r = 0.61–0.71). The STMS total score was significantly correlated with all neuropsychological test scores (r = 0.30–72). Conclusions The STMS shows strong concordance with traditional neuropsychological tests in a dementia clinic sample. Future analyses will include examining construct validity of the STMS with different clinical groups and diagnoses as well as an exploration of the underlying factor structure of the STMS.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".