A preliminary study of the Saint Louis University Mental Status examination (SLUMS) for the assessment of cognition in moderate to severe traumatic brain injury patients
Bibliographic record
Abstract
OBJECTIVES: To compare the sensitivity and specificity of the Saint Louis University Mental Status (SLUMS) examination, the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) in adults with moderate to severe traumatic brain injury (TBI). METHODS: In this cross-sectional study, 98 patients with moderate to severe TBI and 30 matched controls were evaluated. All participants were assessed using the MMSE, the MoCA and the SLUMS examination. RESULTS: The SLUMS, MoCA and MMSE scores of the TBI group were significantly lower than those of the control group, indicating that the cognitive function of patients with TBI was significantly impaired. The receiver operating characteristic (ROC) curve analysis indicated that the areas under the curve for the SLUMS examination, the MoCA and the MMSE were all greater than 0.8. There were no significant differences among the instruments, indicating that all three were equally effective for diagnosing cognitive impairment in patients with moderate to severe TBI. According to the ROC curve analysis, the optimal cutoff values for the SLUMS examination, the MoCA and the MMSE were 24.5, 21.5 and 28.5, respectively. At that cutoff value, the sensitivity and specificity of the SLUMS examination were well balanced, with both exceeding 80%. CONCLUSIONS: The SLUMS examination is better suited than the MMSE or the MoCA for assessing cognitive function in patients with moderate to severe TBI.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".