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Record W2367987631

The Efficacy of Aspirin and Acetaminophen in the Management of Delayed Onset Muscle Soreness

2002· article· en· W2367987631 on OpenAlexaboutno aff
Ki Hyun Kim, Yeoun-Seng Kang, Hyun Seok, Jun-Rae Noh, Jae-Ho Moon

Bibliographic record

VenueAnnals of Rehabilitation Medicine · 2002
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDelayed onset muscle sorenessAcetaminophenAspirinPlaceboVisual analogue scaleAnesthesiaAntacidPhysical therapyMcGill Pain QuestionnaireInternal medicineMuscle damage
DOInot available

Abstract

fetched live from OpenAlex

Objective: To investigate the efficacy of commonly available analgesics in the management of delayed-onset muscle soreness (DOMS) over an 8-day period, and to compare the efficacy between aspirin and acetaminophen. Method: Forty-two subjects were recruited. DOMS was induced by using the isokinetic dynamometer (KinCom) in standardized fashion in the nondominant knee extensor with subjects seated at 30 degree-angle velocity. Subjects were asked to extend their non-dominant knee with concentric method and to hold the knee with eccentric flexion force at 30 degree-angle velocity, with maximal efforts. On this way, they did 10 repetitions, and then 3 cycles. We categorized four groups (n=10, for each group), that were control group with no medication, placebo group with placebo medication (antacid tablets), aspirin group with medication of 900 mg of aspirin, and acetaminophen group with medication with 3,900 mg of acetaminophen. Visual Analogue Scale (VAS: twice a day, until on day 8). and McGill Pain Questionnaire (MPQ: on day 1 and 3) were measured. Results: We didn't find any significant difference of peak VAS score and relief time between four groups (P>0.05), The score of MPQ was not different between four groups (P>0.05). Conclusion: We concluded that the medication may not be beneficial, at least at the doses stated, in the management of DOMS.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.062
GPT teacher head0.358
Teacher spread0.296 · 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
Published2002
Admission routes1
Has abstractyes

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