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Record W4389117990 · doi:10.17509/jpjo.v8i2.57319

Correlation between Running Surface Types and Risk of Iliotibial Syndrome

2023· article· en· W4389117990 on OpenAlexaboutno aff
Rizki Setiawan, Farid Rahman

Bibliographic record

VenueJURNAL PENDIDIKAN JASMANI DAN OLAHRAGA · 2023
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
FundersUniversitas Muhammadiyah Surakarta
KeywordsRespondentIncidence (geometry)Physical therapyTest (biology)MedicinePsychologyPhysical medicine and rehabilitationMathematics

Abstract

fetched live from OpenAlex

Running is a mostly performed sport by people, but it is important to pay attention to the running surface because it can be a factor of injuries experienced by runners. Running on a road runner track is preferred because it can reduce injuries by 32.4%. A study from the Vancouver Running Clinic, the frequency of experiencing Iliotibial Band Syndrome (ITBS) was 6.8% for men and 9.8% for women. This research was aimed to determine the relationship between the type of running surface and Iliotibial Band Syndrome (ITBS) injuries. This research is a correlational study using cross sectional approach. In this research, the non-probability sampling and incidental sampling techniques were used. This research involved 80 runners in the Special Region of Yogyakarta and Magelang City as subjects. The data collection instruments for this research included the Ober test for ITBS and a questionnaire for respondent history. Analysis of data regarding the relationship between the type of running surface and the incidence of Iliotibial Band Syndrome found no relationship. It shows that the type of running surface does not affect the incidence of Iliotibial Band Syndrome (ITBS) in runners. It is expected that this research can become a baseline or foundation for developing prevention of Iliotibial Band Syndrome.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.065
GPT teacher head0.414
Teacher spread0.349 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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