Examining the Vietnamese Montreal cognitive assessment in healthy and moderate-to-severe traumatic brain injury populations
Bibliographic record
Abstract
Objective: There have been attempts to modify the Montreal Cognitive Assessment (MoCA), a brief cognitive screening tool, for use across several Asian countries, but evidence to support the utility of these translations has been limited, particularly for the Vietnamese translation of the MoCA (MoCA-V). This two-part study aimed to evaluate the MoCA-V in a Vietnamese sample. Methods: In the first stage, we examined the relationships between the MoCA-V subscales and common neuropsychological tests among healthy Vietnamese adults (n = 129) and individuals with moderate-to-severe traumatic brain injury (n = 80). In the second stage, we explored the relationship of TBI status (TBI vs non-TBI) and demographic variables to MoCA-V performance and investigated the optimal cut-off score of the MoCA-V using the two samples combined. Results: The MoCA-V Attention, Language, and Executive Function subscales were correlated with the Digit Span Test, Verbal Fluency Test, and Trail Making Test, respectively, across healthy participants and participants with TBI. Global performance on the MoCA-V was predicted by TBI status, education, and age. Our ROC analysis revealed that a cut-off score of 22 offered the best sensitivity (76.3%) and specificity (71.3%) trade-off for identifying cognitive impairment as measured by the MoCA-V. Conclusions: In addition to identifying a cut-off score for cognitive screening, the findings provide support for the validity of the examined MoCA-V subscales and for the MoCA-V’s ability to distinguish TBI survivors vs controls. These results may pave the way for larger-scale investigations of the MoCA-V and for the development of more neuropsychological batteries in Vietnamese.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".