Neuromuscular Control and Exercise-Related Leg Pain in Triathletes
Bibliographic record
Abstract
UNLABELLED: Previous studies have shown that cycling can directly influence neuromuscular control during subsequent running in some highly trained triathletes. A relationship between this altered neuromuscular control of running and musculoskeletal pain and injury has been proposed; however, this link has not been investigated. PURPOSE: This study aimed to evaluate the influence of cycling on neuromuscular control during subsequent running in highly trained triathletes with and without exercise-related leg pain (ERLP). METHODS: Participants were 34 highly trained triathletes: 10 triathletes with a history of ERLP and 24 training-matched control triathletes with no history of ERLP. Knee and ankle kinematics and leg muscle recruitment were compared between a baseline run (no prior exercise) and a transition run (preceded by cycling; i.e., run vs cycle run). RESULTS: Knee and ankle joint kinematics were not different between baseline and transition runs for any triathletes: absolute mean difference (+/-95% confidence interval) was 1.49 degrees +/- 0.17 degrees. However, muscle recruitment was different between baseline and transition runs, defined by absolute mean difference in EMG amplitude > or = 10%, in 5 of 24 control triathletes (11/130 muscles exhibited altered recruitment) and in 5 of 10 triathletes with a history of ERLP (12/50 muscles exhibited altered recruitment). This represents a relative risk of 2.40 (0.89-6.50; P = 0.089) when defined by athletes and 2.62 (1.34-6.01; P < 0.01) when defined by muscles. The magnitude of change in muscle recruitment between baseline and transition runs was not different between control (14.10% +/- 2.34%) and ERLP triathletes (16.31% +/- 3.64%; P = 0.41). CONCLUSIONS: This study demonstrates an association between ERLP in triathletes and their neuromuscular control when running off the bike.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".