A Systematic Review of Brief Cognitive Test Used Among the Veteran Population
Bibliographic record
Abstract
The practice problem this DNP project addressed was the lack of a of consensus for a brief cognitive screening (BCS) tool that can be most predictive of mild cognitive disorder (MCI) and dementia in a Veteran Affairs (VA) outpatient clinic. Addressing this gap in practice is important because the veteran population may be at increased risk of MCI and dementia due to age, a history of traumatic brain injury, and posttraumatic stress disorder. This DNP project was a systematic review of the literature. A population, exposure, outcome (PEO) framework and the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) theory guided this project. Databases were searched for literature published from 2010 to 2024. Five papers involving a VA patient population were identified from an initial pool of 50 and analyzed for psychometric data and criteria using the PEO framework. Sensitivity was reported for three tools, VA Medical Center Saint Louis University Mental Status (VAMC SLUMS; 0.742), Montreal Cognitive Assessment (MOCA; 0.677), and the Short Test of Mental Status (STMS; 0.613), respectively. The Clock Drawing Test (CDT) and Trail Making Test (TMT) had high specificity and variable sensitivity. MCI detection data were not available for the Mini Mental State Examination (MMSE) and Rapid Cognitive Screen (RCS). Dementia detection data were available for the MMSE only. Cognitive screening tools developed for, or validated in, the VA environment, such as the VAMC SLUMS exam and the RCS, align more closely with the operational and demographic needs of veteran care. The implications for nursing practice and social change are that the use of a preferred BCS tool for the VA outpatients may lead to improved detection of MCI and dementia.
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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 | 0.015 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".