Dual-Task Assessment Protocols in Concussion Assessment: A Systematic Literature Review
Bibliographic record
Abstract
Study Design Systematic review. Background When assessed in isolation, balance and neurocognitive testing may not be sufficiently responsive to capture changes that occur with concussion. Normal daily activities require simultaneous cognitive and physical demands. Therefore, a dual-task assessment paradigm should be considered to identify performance deficits. Objectives To evaluate the literature and to identify dual-task testing protocols associated with changes in gait after concussion. Methods A systematic review of articles of individuals with concussion who underwent dual-task testing with a combination of motor and cognitive tasks was conducted. The AMED, CINAHL, Embase, PsycINFO, PubMed, Scopus, SPORTDiscus, and Web of Science databases and gray literature were searched from inception to January 29, 2017. Title and abstract, full-text, and quality review and data abstraction were performed by 2 independent reviewers. Results Twenty-four articles met the inclusion criteria. Eleven articles reported decreased gait velocity and increased medial-lateral displacement for individuals with concussion during dual-task conditions. Overall, included articles were of poor to moderate methodological quality. Fifteen articles used the same participants and data sets, creating a threat to validity and limiting the ability to make conclusions. Conclusion A deterioration in gait performance during dual-task testing is present among people with concussion. Specific recommendations for the use of a dual-task protocol to assess individuals with suspected concussion injury in a clinical setting have yet to be determined. J Orthop Sports Phys Ther 2018;48(2):87-103. Epub 7 Nov 2017. doi:10.2519/jospt.2018.7432.
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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 | 0.030 | 0.120 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".