Preliminary study of the validity and reliability of the Chinese version of the Saint Louis University Mental Status Examination (SLUMS) in detecting cognitive impairment in patients with traumatic brain injury
Bibliographic record
Abstract
The Saint Louis University Mental Status Examination (SLUMS) has been shown to be useful in the cognitive assessment in older adults and patients with dementia. The aim of this study was to preliminarily explore the effectiveness of the Chinese version of the SLUMS in the detection of cognitive impairment in patients with traumatic brain injury (TBI) and to provide an objective basis for its clinical application in China. In this cross-sectional study, 42 patients with TBI and 30 matched normal controls were administered. Participants were assessed by the Chinese version of the Mini-Mental State Assessment scale (MMSE), Montreal Cognitive Assessment scale (MoCA) and SLUMS. Results showed that the Chinese version of the SLUMS had satisfactory internal consistency (Cronbach's α coefficient: 0.723), excellent interrater reliability (ICC: 0.990–0.998) and intrarater reliability (ICC: 0.968), as well as good validity. In the TBI group, the total SLUMS score was moderately positively correlated with the MMSE score (r = 0.702, p = .000) and highly positively correlated with the MoCA score (r = 0.831, p = .000). Receiver Operating Characteristic (ROC) curve analyses showed that the area under the curve (AUC) of the SLUMS, MMSE and MoCA were 0.872, 0.756 and 0.916, respectively. The optimal cutoff score of 22.5 or fewer points are suggested for the SLUMS to discriminate cognitive impairment, with a sensitivity of 0.844 and a specificity of 0.825. The Chinese version of the SLUMS has excellent reliability and validity, and can be used as a screening tool for cognitive impairment of patients with TBI in China.
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.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".