Effect of fatigue on knee biomechanics during the sidestep cutting manoeuvre: A modelling approach
Bibliographic record
Abstract
Loading both lateral and medial compartments is crucial to understanding the effect of muscle fatigue during sidestep cutting. The present study investigated the changes in tibiofemoral contact forces in the medial and lateral compartments and the muscle force contributions during the sidestep-cutting manoeuvre after a handball-specific fatigue protocol. Twenty female handball athletes performed three trials of the sidestep-cutting manoeuvre before (baseline) and after the fatigue protocol. Motion capture and ground reaction forces were measured, and the data were processed in OpenSim. The variables were compared using statistical parametric mapping (SPM), with a significance level of p < 0.05. The results showed a decreased knee flexion angle during fatigue in the early stance phase. In addition, the post-fatigue analysis demonstrated significantly reduced forces in vasti muscles. Similarly, during fatigue, the SPM analysis showed decreased tibiofemoral contact forces in the vertical and anterior directions. Vertical force applied to both medial and lateral condyles demonstrated a significant reduction after the fatigue protocol. These results indicated that forces applied to the tibiofemoral joint were reduced following the fatigue protocol compared to the baseline values. However, no consistent evidence exists that fatigue increases the risk of knee injuries.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".