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Record W2799689886 · doi:10.1155/2018/4523849

Plantar Stress-Related Injuries in Male Basketball Players: Variations on Plantar Loads during Different Maximum-Effort Maneuvers

2018· article· en· W2799689886 on OpenAlexaff
Yiyang Chen, Jing Xian Li, Youlian Hong, Lin Wang

Bibliographic record

VenueBioMed Research International · 2018
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Ottawa
FundersNational Natural Science Foundation of China
KeywordsForefootBasketballHeelMedicineOrthodonticsFoot (prosody)Stress fracturesAthletesPlantar pressurePhysical medicine and rehabilitationPhysical therapySurgeryEngineeringAnatomy

Abstract

fetched live from OpenAlex

This study aims to compare the insole load of three maximum-effort cutting tasks in basketball. Sixteen male basketball players were recruited to participate in the study. The Pedar Mobile system was used to record the insole plantar load distribution during three cutting tasks (45° cutting, 90° cutting, and sideward cutting). The peak pressures (PP) and maximum force (MF) at the total foot and at each foot mask were used in data analysis. ANOVA with repeated measures was employed to investigate the differences in the measures among these cutting tasks. At the total foot, the highest MF value was showed when performing sideward cutting. At the heel, the highest PP and MF were found when performing 90° cutting. The PP and MF were lower when performing 90° cutting than when conducting 45° and sideward cuttings at the medial midfoot and the central forefoot. Furthermore, the MF value was lower when performing 45° cutting than when conducting sideward cutting at the medial midfoot and the central forefoot. These findings corroborate the fact that plantar loads differed during the three maximum-effort cutting maneuvers. Differences in the plantar loads for different cutting may be potential risks for overuse-related injuries to the lower extremities of basketball players.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.035
GPT teacher head0.361
Teacher spread0.327 · 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.

Study designObservational
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

Citations11
Published2018
Admission routes1
Has abstractyes

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