Neuromuscular patterns in chronic ankle instability
Bibliographic record
Abstract
Background Ankle sprains are the most frequent injury in sports. Up to 40% of acute sprains develop chronic ankle instability (CAI). CAI may be seen either as mechanical (ligamentous) instability or functional (neuromuscular) instability. So far, no method is known to objectivate functional instability. Purpose To measure neuromuscular patterns in patients with mechanical ligamentous CAI. Design Prospective cohort study. Setting and patients 15 Patients (age 15–58 years) were included evidencing chronic unilateral mechanical ankle instability undergoing ligament reconstruction. As control, the contralateral healthy ankle and 15 sex- and age-matched participants were used. Interventions Superficial Electromyography (EMG) analysis of four muscles were recorded: gastrocnemius medialis (GM), tibialis anterior (TA), peroneus longus (PL) and soleus (SO) while the patient performed single stance tests on a Biodex Balance System (BBS). Main outcome measurements EMG was analyzed by intensity and frequency using Wavelet Transformation. Neuromuscular controal was analyzed by the results of the BBS. Results CAI patients showed significantly decreased results in neuromuscular ankle joint control correlating to increased ankle pain. EMG analyses showed a significant decrease in frequency recruitment for the PL (average frequency, 138.3 Hz for the unstable ankle versus 158.3 Hz for the contralateral healthy side, p<0.001). Instead, no change in intensity was found for the PL. For the GM, TA, and SO, changes neither for intensity, nor frequency were found. Conclusion This shift of frequency in the PL muscle due to chronic ligamentous ankle instability is the sign of lower muscle activation coming along with type II muscle atrophy. This is the first study to report on the fact that functional ankle instability may be measured as muscle damage seen by a shift in EMG frequency. Therefore, prevention and rehabilitation of chronic ligamentous instability has to address also a functional chronic muscle damage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".