Intrasession and Intersession Reliability of Quadriceps' and Hamstrings' Electromyography During a Standardized Hurdle Jump Test With Single Leg Landing
Bibliographic record
Abstract
The objective of this study was to develop a standardized test to determine quadriceps and hamstrings muscle activation in a position emulating a noncontact anterior cruciate ligament injury. We assessed the intrasession and intersession reliability of surface electromyography (EMG) of the dominant leg after single-leg landing from a standardized hurdle jump. Eighteen subjects (10 males, 8 females) participated in 4 repeated sessions. During each session, individuals performed 3 successful jumps over a hurdle set to 75% of their maximal countermovement jump height and landed on their dominant leg. A jump was only considered successful if the individual could maintain the landing position for longer than 2 seconds after initial ground contact. In one of the 4 sessions, subjects were tested again after a 4-minute rest. The activation of the vastus lateralis (VL), vastus medialis (VM), and biceps femoris (BF), were examined by quantifying the root mean squared (RMS) EMG for 2 seconds immediately after the initial contact. Data from all 3 successful jumps were used to generate intraclass correlation coefficients (ICC), which were then used to determine intrasession and intersession reliability of surface EMG for each muscle. Intrasession reliability was excellent with ICC values of 0.96, 0.94, and 0.93 for the VL, VM, and BF, respectively. Additionally, intersession ICCs were 0.92 (VL), 0.95 (VM), and 0.94 (BF). The standardized hurdle jump with single-leg landing seemed to be a reliable technique for measuring muscle activation for 3 muscles that contribute to knee stabilization.
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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 | 0.001 | 0.005 |
| 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".