Neurocognitive Assessment Tools for Military Personnel With Mild Traumatic Brain Injury: Scoping Literature Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Mild traumatic brain injury (mTBI) occurs at a higher frequency among military personnel than among civilians. A common symptom of mTBIs is cognitive dysfunction. Health care professionals use neuropsychological assessments as part of a multidisciplinary and best practice approach for mTBI management. Such assessments support clinical diagnosis, symptom management, rehabilitation, and return-to-duty planning. Military health care organizations currently use computerized neurocognitive assessment tools (NCATs). NCATs and more traditional neuropsychological assessments present unique challenges in both clinical and military settings. Many research gaps remain regarding psychometric properties, usability, acceptance, feasibility, effectiveness, sensitivity, and utility of both types of assessments in military environments. OBJECTIVE The aims of this study were to explore evidence regarding the use of NCATs among military personnel who have sustained mTBIs; evaluate the psychometric properties of the most commonly tested NCATs for this population; and synthesize the data to explore the range and extent of NCATs among this population, clinical recommendations for use, and knowledge gaps requiring future research. METHODS Studies were identified using MEDLINE, Embase, American Psychological Association PsycINFO, CINAHL Plus with Full Text, Psych Article, Scopus, and Military & Government Collection. Data were analyzed using descriptive analysis, thematic analysis, and the Randolph Criteria. Narrative synthesis and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews) guided the reporting of findings. The psychometric properties of NCATs were evaluated with specific criteria and summarized. RESULTS Of the 104 papers, 33 met the inclusion criteria for this scoping review. Thematic analysis and NCAT psychometrics were reported and summarized. CONCLUSIONS When considering the psychometric properties of the most commonly used NCATs in military populations, these assessments have yet to demonstrate adequate validity, reliability, sensitivity, and clinical utility among military personnel with mTBIs. Additional research is needed to further validate NCATs within military populations, especially for those living outside of the United States and individuals experiencing other conditions known to adversely affect cognitive processing. Knowledge gaps remain, warranting further study of psychometric properties and the utility of baseline and normative testing for NCATs.
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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 | 0.009 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".