Muscle activation characteristics in cross-country skiers with a history of anterior compartment pain
Bibliographic record
Abstract
A large proportion of elite cross-country skiers suffer from chronic anterior compartment syndrome (CACS). This study used surface electromyograms (EMGs) to investigate whether differences existed in the activation characteristics of the tibialis anterior muscle between elite cross-country skiers with a history of anterior compartment pain (symptomatic group) and a pain-free control group. Based on self-reported pain symptoms, twelve young, national-level cross-country ski athletes were assigned to a symptomatic group (N = 5), a control group (N = 4), or analyzed individually if their diagnosis was not certain (N = 3). During skating, EMGs were recorded on five lower leg muscles. The relative increase in EMG power per step when increasing the effort level of skating was larger in the symptomatic group than in the control group for tibialis anterior (143 +/- 12% vs. 125 +/- 23%; Cohen's d = 1.17), peroneus longus (123 +/- 24% vs. 107 +/- 6%; d = 0.91), and gastrocnemius lateralis (167 +/- 51% vs. 117 +/- 12%; d = 1.64). The symptomatic group showed more power in the lower frequency bands of the tibialis anterior's EMG spectra (p < 0.001), whereas no group differences were found in other muscles (all p > 0.2). Within the step cycle, these differences appeared in the swing phase and in the gliding phase during single leg support. The observed differences in the EMG spectra may serve as an early identification of athletes who are at risk of developing CACS.
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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 | 0.000 |
| 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) | 0.002 | 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".