Performance fatigability and recovery after dynamic multi-joint maximal exercise in elbow flexors versus knee extensors
Bibliographic record
Abstract
Elbow flexors (EFs) and knee extensors (KEs) have shown differences in performance fatigability and recovery of neuromuscular function after isometric and isotonic single-joint fatiguing contractions. However, dynamic multi-joint movements are more representative of real-world activities. The aim of the study was to assess central and peripheral mechanisms of fatigability after either arm-cranking or cycling. Ten physically active men performed maximal incremental arm-cranking and cycling until task failure. Maximal voluntary isometric contraction (MVIC) and electrically evoked forces of both EF and KE were assessed before (PRE) and 1 (POST) and 20 (POST20) min after exercise. At POST, MVIC decreased similarly to 76 ± 8% and 81 ± 7% (both P < 0.001) of PRE for EF and KE, respectively. MVIC force remained lower than PRE at POST20 for both EF and KE (85 ± 8% vs. 95 ± 3% of PRE, P ≤ 0.033), having recovered less in EF than in KE ( P = 0.003). Electrically evoked forces decreased similarly from PRE to POST in EF and KE (all P > 0.05). At POST20, the ratio of low-to-high frequency doublets was lower in EF than in KE (75 ± 13% vs. 85 ± 10% of PRE; P ≤ 0.034). Dynamic maximal incremental exercise acutely induced similar magnitudes of MVIC and evoked force loss in EF and KE. However, at POST20, impaired MVIC recovery and lower ratio of low-to-high frequency doublets in EF than in KE suggest the recovery of neuromuscular function after dynamic maximal exercises is specific to and dependent on changes within the muscles investigated.
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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 | 0.000 | 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) | 0.002 | 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".