Clinical utility of the Saint Louis University Mental Status Examination (SLUMS) in a mixed neurological sample: Proposed revised cutoff scores for normal cognition, mild cognitive impairment, and dementia
Bibliographic record
Abstract
Early detection of cognitive impairment is of paramount importance in clinical settings, with several brief screening tools having been developed for that purpose. The present study sought to evaluate the clinical utility of the Saint Louis University Mental Status examination (SLUMS) at identifying examinees with normal cognition, mild cognitive impairment, or dementia syndrome using the criterion of a comprehensive neuropsychological assessment. Two hundred sixty-three examinees (M age = 67.84 ± 12.72; 59.3% female; 81.4% white) were referred for comprehensive neuropsychological evaluation at a private, Mid-Atlantic medical center. Using original cutoff scores, the SLUMS correctly classified just over half (55.1%) of examinees. Classification statistics suggested modified cutoff scores for mild cognitive impairment (≤24) and dementia (≤17) with strong discriminability between cognitive status groups (AUCs ranged from .834 to .986). These proposed revised cutoff scores improved overall concordance between SLUMS and diagnostic conclusions from comprehensive clinical neuropsychological testing, correctly classifying nearly two-thirds of examinees (65.4%). The SLUMS and its revised cutoff scores appear to have clinical utility for cognitive screening in primary care and neurological settings to inform treatment plans and appropriate referrals for comprehensive neuropsychological assessment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".