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Record W4403138452 · doi:10.3390/s24196427

Effect of Unanticipated Tasks on Side-Cutting Stability of Lower Extremity with Patellofemoral Pain Syndrome

2024· article· en· W4403138452 on OpenAlexaff
Yiwen Ma, Wenjing Quan, Xuting Wang, Julien S. Baker, Zixiang Gao, Yaodong Gu

Bibliographic record

VenueSensors · 2024
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Calgary
FundersNatural Science Foundation of Ningbo
KeywordsPatellofemoral pain syndromePhysical medicine and rehabilitationAnkleMedicineRehabilitationPhysical therapyKnee JointPatellofemoral jointJoint stabilityOrthodonticsPatellaSurgery

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.229
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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