Testing Limb Symmetry and Asymmetry After Anterior Cruciate Ligament Injury: 4 Considerations to Increase Its Utility
Bibliographic record
Abstract
ABSTRACT Anterior cruciate ligament (ACL) injury occurs frequently in sport and surgical reconstruction is often recommended to restore knee joint stability. To guide rehabilitation and determine return to sport readiness, practitioners have used a long-standing practice of calculating the limb symmetry index (LSI) in various functional, biomechanical, and strength tests to compare the injured limb with the noninjured contralateral limb. However, the evidence in support of the LSI calculation to quantify rehabilitation status and return to sport readiness is mixed. We synthesize scientific literature on the LSI calculation and discuss potential reasons for the mixed evidence and limitations. We present 4 considerations to improve the utility of the LSI calculation including (a): the importance of establishing the right benchmark of recovery such as the preinjury contralateral limb or a sport-specific noninjured control benchmark; (b) strategies to manage the high variation in movement asymmetry calculations and the importance of quantifying the intrasubject variability for the component parts of the LSI; (c) the evidence for assessing the movement strategy alongside performance when using the LSI; and (d) how a sport-specific envelope of function can be used to inform post-ACL injury testing that incorporates the LSI.
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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 | 0.072 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".