A comparison of test-retest reliability of four cognitive screening tools in people with dementia
Bibliographic record
Abstract
Purpose This study aimed to compare the test-retest reliability and minimal detectable change (MDC) of the Mini-Mental State Examination (MMSE), the Short Portable Mental Status Questionnaire (SPMSQ), the Montreal Cognitive Assessment (MoCA), and the Saint Louis University Status Examination (SLUMS) in a single sample of people with dementia.Methods Sixty people with dementia were assessed twice two weeks apart, and the test-retest reliability was examined using the intraclass correlation coefficient (ICC) for four screening tools. The MDC95 value was calculated based on the standard error of measurement to estimate the random measurement error.Results The ICC values for screening tools were 0.86–0.90. The MDC95 values (MDC95%) were 5.0 (17.2%), 2.74 (27%), 4.71(20%), and 6.26 (24%) for the MMSE, SPMSQ, MoCA, and SLUMS, respectively.Conclusions Overall, the four screening tools were similar in test-retest reliability which imply that the MMSE, MoCA, SPMSQ, and SLUMS were reliable in monitoring cognitive function in people with dementia. The results of the direct comparisons of test-retest reliability of the four screening tools provide useful information for both clinicians and researchers to select an appropriate cognitive screening tool.Implications for RehabilitationThe MMSE, MoCA, SPMSQ, and SLUMS are equally reliable and thus they could be used to monitor the cognitive function in people with dementia.The MDC values are useful in determining whether a real change has occurred between repeated assessments for people with dementia.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".