Effects of Two Fatigue Protocols on Impact Forces and Lower Extremity Kinematics during Drop Landings: Implications for Noncontact Anterior Cruciate Ligament Injury
Bibliographic record
Abstract
The purpose of the study was to determine the effects of fatigue on the impact forces and sagittal plane kinematics of the lower extremities in a drop landing task. 15 male collegiate athletes were recruited. Five successful trials of a drop landing task were obtained during prefatigue and postfatigue in two fatigue protocols (constant speed running fatigue protocol [R-FP] and shuttle running + vertical jumping fatigue protocol [SV-FP]). Duration time, maximal heart rate, and RPE of each protocol were measured separately. Kinematic measures of the hip, knee, and ankle joints at different times coupled with peak impact force and loading rate were acquired. Our results showed a more flexed landing posture due to an increase in hip and knee flexion angles in the postfatigue condition. However, no differences in peak impact force and loading rate were found between pre- and postfatigue conditions. The changes were similar between protocols, but the SV-FP showed a significantly shorter exercise duration time than the R-FP. Fatigued athletes in this study demonstrated altered motor control strategies during a drop landing task, which may be an intentional or unintentional protective strategy for preventing themselves from potential ACL injury.
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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 | 0.001 |
| 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) | 0.001 | 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".