Ankle sprain history and clinical outcome have limited influence on walking and running biomechanics among runners: a cross-sectional study
Bibliographic record
Abstract
Background: Lateral ankle sprain (LAS) is prevalent among runners, with many developing chronic ankle instability (CAI). While CAI is associated with many motor-behavioral, sensory-perceptual, and pathomechanical factors, its impact on gait biomechanics remains unclear. This cross-sectional study aimed to assess gait biomechanics and other factors contributing to CAI in runners. Methods: = 13). Walking and running spatiotemporal, kinetic and kinematic parameters were collected on an instrumented treadmill. Rehabilitation-oriented assessment outcomes were also assessed. One-way ANOVA or Kruskal-Wallis tests were used, along with their corresponding post-hoc tests. Effect sizes (g or r according to normality) were reported. Results: = 0.47-0.67) than healthy controls and copers. However, running biomechanics did not differ between groups, suggesting that traditional biomechanical assessments at comfortable speeds may not be sensitive to functional deficits in CAI. A notable finding was the lower mechanical work recovery during walking in copers compared to healthy controls (g = 0.98). Conclusion: These results highlight the importance of considering self-reported function and perceived instability when assessing LAS and CAI. The absence of gross running gait alterations suggests that rehabilitation could safely integrate running early in recovery. However, more demanding tasks or advanced biomechanical modeling techniques may be needed to identify residual gait impairments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".