Gait Deviations Associated With Concussion: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Gait deviations resulting from concussion are important to consider in the diagnosis, treatment progression, and return to activity after a concussion. OBJECTIVE: To identify quantifiable gait deviations associated with concussion across populations and time since injury. METHODS AND MATERIALS: Six electronic databases were systematically searched from January 1974 to September 2016. Studies selected included original data, had an analytic design, and reported a quantifiable gait parameter in individuals who had sustained a concussion as defined by the American Congress of Rehabilitation Medicine or related definitions. Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines were followed. Two independent authors assessed study quality [Downs and Black (DB) criteria] and level of evidence (Oxford Center of Evidence-Based Medicine Model). RESULTS: Of 2650 potentially relevant articles, 21 level 4 studies were included. The median DB score was 12/33 (range 10-16). Heterogeneity in gait parameters and timing of postconcussion testing precluded meta-analysis. There is consistent level 4 evidence of increased medial-lateral center-of-mass displacement, and inconsistent level 4 evidence of decreased gait velocity after concussion. Further, there is preliminary level 4 evidence that gait deficits may exist beyond the typical 10-day recovery period and return to activity. CONCLUSION: These findings suggest that individuals who have suffered a concussion may sway more in the frontal plane, and walk slower compared to healthy controls. Consensus about the most important gait parameters for concussion diagnosis and clinical management are lacking. Further, high-quality prospective cohort studies evaluating changes in gait from time of concussion to return to activity, sport, recreation and/or work are needed.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".