Reliability and validity of the Rowland Universal Dementia Assessment Scale for patients with traumatic brain injury
Bibliographic record
Abstract
Objective and accurate cognitive assessment scales are essential for guiding cognitive rehabilitation following traumatic brain injury (TBI). The aim of this study was to evaluate the reliability and validity of the Rowland Universal Dementia Assessment Scale (RUDAS) for TBI and to verify the clinical application value. Fifty patients with TBI and 32 matched controls were assessed using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and a newly developed Chinese version of RUDAS. These scales were then compared for internal consistency, inter-rater reliability, test‒retest reliability, content validity, construct validity, and diagnostic efficacy. Among the TBI group, the RUDAS demonstrated acceptable internal consistency (Cronbach’s α = 0.733), high inter-rater reliability (intraclass correlation coefficients [ICCs] of 0.910‒0.999), and high test‒retest reliability (total score ICC = 0.938). The correlation coefficients between RUDAS total score and individual subscores were all > 0.5 except for body orientation (r = 0.363), indicating generally good content validity. Total RUDAS scores were moderately correlated with both MMSE total scores (r = 0.701, p < 0.001) and MoCA total scores (r = 0.778, p < 0.001), indicating good construct validity. Receiving operating characteristic curve analysis yielded comparable areas under the curve for diagnostic efficacy (RUDAS, 0.844; MMSE, 0.769; MoCA, 0.824; all p > 0.05). A RUDAS score cutoff of 23.5 distinguished TBI patients from controls with 60% sensitivity and 100% specificity. Therefore, the RUDAS demonstrates both good reliability and validity for evaluating cognitive impairments in TBI patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".