A PRELIMINARY ANALYSIS OF THE IMPACT OF PREVIOUS KNEE INJURY ON MEASURES OF BALANCE AND THEIR IMPLICATIONS FOR SECONDARY PREVENTION
Bibliographic record
Abstract
Background Individuals with a history of previous injury have been shown to be at higher risk for re-injury. It is plausible that this is due to balance deficits which have been identified as injury risk factors in multiple sports. Objective To quantify the impact of previous knee joint injury on clinical and force plate measures of balance. Design Historical Cohort. Setting University of Calgary Human Performance Laboratory. Participants 25 young adults with a 3–10 year history of intra-articular knee joint injury and 25 matched (age, sex and sport) uninjured controls (34 males: 17-26 yrs; 16 females: 14-26 yrs). Assessment of risk factors Intra-articular knee injury was defined as a clinical diagnosis of bone, cartilage, ligament or meniscal injury that required medical consultation and disrupted sport participation. Main outcome measures Star excursion balance test (SEBT), medio-lateral entropic half-life (EHL-ML), anterior-posterior entropic half-life (EHL-AP), and center of pressure 95% ellipse area (EA). Force plate measures were quantified during single leg quiet standing. Results Injured participants had longer EHL-ML compared to healthy controls [Injured: 103 ms (95% CI: 97–109), Uninjured 94 ms (95% CI: 88–100), (paired t=2.031, P=.048)]. No group differences were found for the remaining balance measure [SEBT: Injured 76% (95% CI: 74–79), Uninjured 79% (95% CI: 77,80), (paired t=1.413, P=.164); EHL-AP: Injured 169 ms (95% CI: 148–190), Uninjured 152 ms (95% CI: 138–167), (paired t=1.289, P=.204); EA: Injured 694 mm2(95% CI: 585–802), Uninjured 597 mm2(95% CI: 505–688), (paired t=1.340, P=.187)]. Conclusions Participants with previous knee injury (3–10 years) were unable to adjust their position as quickly and frequently as healthy controls as indicated by longer EHL-ML. This result suggests that previous injury has an impact on balance ability. These findings support the evaluation of balance to inform the development of interventions to prevent re-injury.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".