The reliability and validity of brief cognitive screening tools used in traumatic brain injury: A systematic review
Bibliographic record
Abstract
Reliable and valid cognitive screening tools are essential in the assessment of those with traumatic brain injury (TBI). Yet, there is no consensus about which tool should be used in clinical practice. This systematic review assessed psychometric properties of cognitive screening tools for detecting cognitive impairment in TBI. Inclusion criteria were: peer-reviewed validation studies of a cognitive screening tool(s); with a sample of adults aged 18-80 diagnosed with TBI (mild-severe); and with psychometrics consistent with COSMIN guidelines. Published literature was retrieved from MEDLINE, Web of Science Core Collection, EMBASE, CINAHL, and PsycINFO on 27 January 2022. A narrative synthesis was performed. Thirty-four studies evaluated the psychometric properties of a total of 22 cognitive screening tools, in a variety of languages. Properties assessed included structural validity, internal consistency, reliability, criterion validity (or diagnostic test accuracy), convergent/divergent validity, and discriminant validity. The Montreal Cognitive Assessment (MoCA) and the Mini Mental State Examination (MMSE) were the most widely validated cognitive screening tools for use in TBI. The MoCA had the most promising evidence of its psychometric properties, which has implications for clinical practice. Future research should aim to follow standard criteria for psychometric studies to allow meaningful comparisons across the literature.
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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 | 0.013 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".