Characterization of perceived muscle soreness and prediction of skeletal muscle markers of damage following a bout of high intensity functional resistance training
Bibliographic record
Abstract
Exercise-induced skeletal muscle damage often resolves in 1-5 days, however severe complications occasionally arise. Identifying predictors of severe muscle damage may reduce potential risks associated with high volume or extended duration workouts. PURPOSE: Determine if pain perception is related to markers of skeletal muscle damage following a standardized HIRFT bout. METHODS: Participants (n=19[13 males, 174.7±7.9 cm, 77.9±13.7 kg, 25.8±6.5 y, training experience 3.5±1.3y] completed a standardized HIRFT workout (1 mile run, 100 pull ups, 200 pushups, 300 air squats, 1 mile run). At 5 timepoints (24h pre, immediately pre, immediately post, 24h post and 48h post exercise), participants completed the Short Form McGill Pain Questionnaire (MPQ), including responses to 15 pain terms and a visual analog scale (VAS). Plasma osmolality (Posm) was measured from blood samples taken at each of the above timepoints. RESULTS: RMANOVA revealed a main effect of time for 11 pain descriptors, VAS, and Mean Plasma Osmolality (all p ≤ .035). A stepwise regression analysis revealed a significant relationship between VAS immediately post exercise and mean plasma osmolality 48h post exercise. CONCLUSION: Eleven MPQ terms best described perceived muscle pain following the HIRFT bout. The relationship between VAS immediately post and plasma osmolality 48h post exercise demonstrates individuals who perceived more pain immediately following workout showed evidence of increased skeletal muscle damage 48h after the workout. This muscle damage may be due to a release of urea from the skeletal muscle cell following exercise. Findings indicate perceived muscle pain may be a valuable predictor of muscle 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".