Gender Differences after Downhill Running for Voluntary Isometric Contractions of Knee Extensor Muscles using Surface EMG
Bibliographic record
Abstract
Introduction:We examined gender differences for performance and surface electromyograms (sEMG) of knee extensors 1hr after downhill running.Methods: Maximal voluntary isometric force (MVIF) and sEMG of vastus lateralis (VL) and vastus medialis (VM) muscles during 50%MVIF were measured in 8 males (22±4yr, 75.8±6.1kg,179.2.0±5.0cm) and 8 females (27±8yr, 60.8±7.2kg,166.3±5.0cm)before and 1 hr after downhill running [(5x8 min, -12%, 60%V max (females: 7.7±0.9km•h - , males: 10.4±0.5km•h - )].Results: Males had higher MVIF values (674.3±59.8Nversus 480.6±49.9N,P<0.001) and longer endurance times during 50%MVIF than females (78.7±24.1sversus 57.2±12.6s,P<0.05) before downhill running.After downhill running, MVIF deficits were similar (males: 11.7±7.2%,females: 7.5±7.7%)with no changes in endurance times during 50%MVIF.After the downhill run, there was a trend in females, but not males, of an increased ratio for the root mean square (RMS) values of the VL and VM muscles during MVIF testing by 23% (P=0.054).After the downhill run, females, but not males, lowered during 50%MVIF the change in RMS for VM from 48±32% to 30±31% and for VL from 36±23% to 21±18% (P<0.05).For males, a trend was observed for the change in median frequency in VL during 50%MVIF (P=0.05).After downhill running, females, altered the vastus medialis and vastus lateralis contributions to maximal isometric force production and the activity pattern of these muscles during submaximal isometric fatigue.Conclusion: Acute neuromuscular adaptations after downhill running occur during voluntary isometric contractions of knee extensor muscles in females, but not in males.
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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) | 0.004 | 0.001 |
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".