Acute and delayed peripheral and central neuromuscular alterations induced by a short and intense downhill trail run
Bibliographic record
Abstract
Downhill sections are highly strenuous likely contributing to the development of neuromuscular fatigue in trail running. Our purpose was to investigate the consequences of an intense downhill trail run (DTR) on peripheral and central neuromuscular fatigue at knee extensors (KE) and plantar flexors (PF). Twenty-three runners performed a 6.5-km DTR (1264-m altitude drop) as fast as possible. The electromyographic activity of vastus lateralis (VL) and gastrocnemius lateralis (GL) was continuously recorded. Neuromuscular functions were assessed Pre-, Post-, and 2-day Post-DTR (Post2d). Maximal voluntary torques decreased Post (∼ -19% for KE, ∼ -25% for PF) and Post2d (∼ -9% for KE, ∼ -10% for PF). Both central and peripheral dysfunctions were observed. Decreased KE and PF voluntary activation (VA), evoked forces, VL M-wave amplitude, and KE low-frequency fatigue were observed at Post. Changes in VL M-wave amplitude were negatively correlated to VL activity during DTR. Changes in PF twitch force and VA were negatively correlated to GL activity during DTR. The acute KE VA deficit was about a third of that reported after ultramarathons, although peripheral alterations were similar. The prolonged force loss seems to be mainly associated to VA deficit likely induced by the delayed inflammatory response to DTR-induced ultrastructural 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".