Effect of Unanticipated Tasks on Side-Cutting Stability of Lower Extremity with Patellofemoral Pain Syndrome
Bibliographic record
Abstract
BACKGROUND: Patellofemoral pain syndrome (PFPS) is one of the most common causes of anterior knee pain encountered in the outpatient setting. The purpose of this study was to compare the lower limb biomechanical differences during anticipated and unanticipated side-cutting in athletes with PFPS. METHODS: Fifteen male basketball players diagnosed with PFPS were enrolled in the study. Participants executed both anticipated and unanticipated 45-degree side-cutting tasks. Motion analysis systems, force plates, and electromyography (EMG) were used to assess the lower limb joint angles, joint moments, joint stiffness, and patellofemoral joint contact forces. Analyzed biomechanical data were used to compare the differences between the two circumstances. RESULTS: Unanticipated side-cutting resulted in significantly increased ankle plantarflexion and dorsiflexion angles, knee abduction and internal rotation angles, and hip abduction angles, as well as heightened knee adduction moments. Additionally, patellofemoral joint contact forces and stress increased, while contact area decreased during unanticipated tasks. CONCLUSIONS: Unanticipated movement raises the demands for joint stability and neuromuscular control, increasing injury risks in athletes with PFPS. These findings have practical implications for developing targeted rehabilitation programs and injury prevention strategies.
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 | 0.002 |
| 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".