Fatigue modifies hip and knee kinematics during single- and double-leg dynamic tasks: An investigation with female handball players
Bibliographic record
Abstract
This study investigated the effect of a handball-specific fatigue protocol on hip and knee kinematics. Twenty female handball athletes performed three trials of the single-leg landing (SLL), sidestep cutting manoeuvre (SCM), and drop vertical jump (DVJ) before and after the fatigue protocol. Knee and hip angle waveforms were compared using statistical parametric mapping (p < 0.05). During the SLL, the fatigue increased hip adduction (4-7% cycle) and knee abduction (4-9% and 25-27%). For the SCM, hip flexion was reduced under fatigue during 14-29% and 44-68% of the cycle. Similarly, the knee flexion decreased between 7-36% and 53-73%. Besides, during the fatigue state, the athletes reduced the hip abduction between 0-11% of the cycle and increased the knee abduction between 20-23%. During the DVJ task, when fatigued, the hip flexion decreased between 19-44% of the cycle and the knee flexion between 1-16% and 18-77%. The fatigue protocol altered the lower limb kinematics, decreasing knee and hip flexions during the SCM and DVJ and increasing the knee valgus during both single-leg landing tasks.
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 | 0.000 |
| 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) | 0.002 | 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".