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Record W4406109337 · doi:10.31943/gw.v15i3.773

The Characteristics of Muscle Soreness – Delayed Onset Muscle Soreness in Sport Person from South Sulawesi

2025· article· en· W4406109337 on OpenAlexaboutno aff
Nurussyariah Nurussyariah, Etno Setyagraha, Nurul Ichsania, Guruh Amir Putra

Bibliographic record

VenueGema Wiralodra · 2025
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsnot available
Fundersnot available
KeywordsDelayed onset muscle sorenessPhysical therapyAthletesMedicineObservational studyMcGill Pain QuestionnairePain scalePhysical medicine and rehabilitationVisual analogue scaleMuscle damageInternal medicine

Abstract

fetched live from OpenAlex

Muscle Soreness – Delayed Onset Muscle Soreness (MS – DOMS) is the immediate and delayed muscle pain experienced by individuals after exercising. This study aims to observe the characteristics of MS-DOMS and the pattern of dietary intake and physical activity of athletes in South Sulawesi. The research method used was an observational description method with cross sectional design using a closed-ended questionnaire given to 211 samples categorized as athletes and non-athletes in South Sulawesi. The questionnaire was distributed using G.Forms and QR codes. The questionnaire used was a modified between the McGill Pain Questionnaire and the numerical pain scale, SQ – FFQ, as well as the form of physical activity performed. The indicators of the questions include physical activity, dietary patterns, and the characteristics of muscle pain. This study took place from May to October 2024. The results obtained showed that 69.2% of respondents experienced muscle pain after exercise, with 44.4% experiencing it less than 6 hours after exercise, with a duration of pain felt for less than 6 hours (44.2%). The most common location of muscle pain was in the thighs (31.8%) with a pain scale of 3 (19%), while the pain felt after 24 hours received the most answers, scale 3 and 5 (17.5%).

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)0.000
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 score0.433
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.271
Teacher spread0.252 · 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
Published2025
Admission routes1
Has abstractyes

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