Investigation of female lower limb muscle reaction to \nmultidirectional perturbation
Bibliographic record
Abstract
Anterior cruciate ligament (ACL) injuries are more prevalent in female athletes compared to male athletes. Researchers have explored the mechanism of an ACL injury in both males and females using mathematical modeling, interview, in-vivo arthroscopy, clinical evaluation, cadaver studies, motion analysis, and electromyography. However, an unexpected perturbation that mimics an ACL injury mechanism has not yet been used. Therefore, this study explored lower extremity muscle activity following an unexpected perturbation that mimics the mechanism of an ACL injury as well as the contribution of the initial stance of the athlete to an injury. \n\tFemale Concordia varsity athletes were recruited in Montreal, QC. Data was collected using the VICON motion capture system, Noraxon DTS EMG, and a goniometer. Participants were asked to maintain balance on their non-dominant leg during unexpected perturbations in the lateral, posterior, and rotational motions as well as a combination motion that mimics an ACL injury mechanism. \n\tThe mean EMG values were greatest during the post-perturbation phase for all muscles compared to the pre-perturbation and perturbation phases for both knee conditions. The time of occurrence of the maximum EMG values revealed that the muscles reached the maximum EMG value later following the onset of perturbation in the rotation direction in order to stabilize the knee joint and/or maintain balance during the lateral, posterior, and combination perturbations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".