Quantifying delayed-onset muscle soreness: A comparison of unidimensional and multidimensional instrumentation
Bibliographic record
Abstract
Unidimensional pain instrumentation, whereby participants simply rate the intensity of their pain on one evaluative level, has been the most common method of assessing delayed-onset muscle soreness (DOMS). However, pain has been shown to be a multidimensional phenomenon including sensory, affective, and evaluative aspects. The aims of this study were two-fold: (1) to compare the DOMS pain responses derived from a multidimensional instrument (i.e. the McGill Pain Questionnaire--MPQ) with those using a unidimensional measure (i.e. a visual analogue scale), and (2) to identify the MPQ descriptors most commonly used to characterize DOMS among a sample of 14 male (mean age = 24.7 years, s = 4.4) and 9 female participants (mean age = 24.6 years, s = 3.5). Although the results demonstrated no significant differences between the pain ratings of the two instruments (mean values of the pain rating indices had a Spearman rank correlation coefficient of r = 1.00), suggesting no significant advantage to be gained in using the MPQ, a clearer description of DOMS emerged. The most frequently selected DOMS descriptors were "tight" (95% of participants chose this descriptor at least once), "sore" (86%), "tender" (86%), "annoying" (86%), and "pulling" (68%). These findings may be of use to researchers and sports medicine professionals in their deliberations about which instrumentation to use in quantifying DOMS and in distinguishing such pain from other, potentially more serious, musculoskeletal damage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".