Electromyographic Assessment of Anterior Cruciate Ligament Injury Risk in Male Tennis Players: Which Role for Visual Input? A Proof-of-Concept Study
Bibliographic record
Abstract
Anterior cruciate ligament (ACL) injury incidence is often underestimated in tennis players, who are considered as subjects conventionally less prone to knee injuries. However, evaluation of the preactivation of knee stabilizer muscles by surface electromyography (sEMG) showed to be a predictive value in the assessment of the risk of ACL injury. Therefore, this proof-of-concept study aimed at evaluating the role of visual input on the thigh muscle preactivation through sEMG to reduce ACL injury risk in tennis players. We recruited male, adult, semiprofessional tennis players from July to August 2020. They were asked to drop with the dominant lower limb from a step, to evaluate—based on dynamic valgus stress—the preactivation time of the rectus femoris (RF), vastus medialis, biceps femoris, and medial hamstrings (MH), through sEMG. To highlight the influence of visual inputs, the athletes performed the test blindfolded and not blindfolded on both clay and grass surfaces. We included 20 semiprofessional male players, with a mean age 20.3 ± 4.8 years; results showed significant early muscle activation when the subject lacked visual input, but also when faced with a less-safe surface such as clay over grass. Considering the posteromedial–anterolateral relationship (MH/RF ratio), tennis players showed a significant higher MH/RF ratio if blindfolded (22.0 vs. 17.0% not blindfolded; p < 0.01) and percentage of falling on clay (17.0% vs. 14.0% in grass; p < 0.01). This proof-of-principle study suggests that in case of absence of visual input or falling on a surface considered unsafe (clay), neuro-activation would tend to protect the anterior stress of the knee. Thus, the sEMG might play a crucial role in planning adequate athletic preparation for semiprofessional male athletes in terms of reduction of ACL injury risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".