The effects of sex and load on quantifying the bilateral force deficit during an upper body Wingate test
Bibliographic record
Abstract
Introduction The bilateral deficit (BLD) is a reduction in the amount of force during a bilateral task vs. the total force from the unilateral limbs performing the same task. We quantified the BLD during an upper body Wingate Anaerobic Test (WAnT) and evaluated the influence of sex and load on the BLD in force. Methods Eighteen participants performed maximum handgrip strength, voluntary isometric contractions (MVIC), and three 30s WAnTs. In each session they completed the tasks with the dominant-arm, non-dominant arm and with both arms, randomly. WAnT intensities were 3, 4, and 5% body weight (BW). Instantaneous force data was used to calculate the BLD. Results Males showed greater (p < .001) BLD of force at 3, 4, and 5% BW than females by −17, −27.6 and −36%, respectively and had a greater (p < .001) BLD of force than females throughout time points 1–10 s, 11–20 s, and 21–30 s by −16, −29 and −35%, respectively. Females showed a difference (p < .001) in BLD of force between loads (−19% at 3%, −10% at 4% and +7% at 5%). Males had an increase (p < .001) in BLD of force from the beginning to the end of the WAnT starting with −18% (1–10 s), −38% (11–20 s) and −40% (21–30 s). WAnT had the highest BLD, followed by MVIC and grip strength. Discussion BLD in force is present during WAnTs and the sex-load interaction is important for determining this BLD during this maximal cycling test. Thus, when developing training or rehabilitation programs related to BLD in force, sex, load and exercise type should be taken into consideration.
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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 | 0.002 |
| 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) | 0.003 | 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".