A Randomized Trial of Therapeutic Ultrasound on Pain, Tenderness, and Muscle Stiffness Using a High and Low Intensity Model of Delayed Onset Muscle Soreness
Bibliographic record
Abstract
Purpose: This study evaluated ultrasound effectiveness in an experimental model of soft tissue injury, and examined the model, delayed onset muscle soreness (DOMS), as a variable in the outcome. Methods: One hundred and twenty females completed 30 repetitions (low-DOMS) or 70 repetitions (high-DOMS) of eccentric contractions of biceps brachii muscles and received one of four protocols: no ultrasound (control), placebo ultrasound, or 3 MHz ultrasound, pulsed 20% duty cycle, at either 0.6 W[Formula: see text]cm2, spatial-average temporal-peak intensity (SATP) (0.12 W[Formula: see text]cm2, spatial-average temporal-average intensity (SATA)) or 1.0 W[Formula: see text]cm2, SATP (0.2 W[Formula: see text]cm2 SATA). A further 60 females completed a low-DOMS protocol and received one of three protocols: placebo ultrasound, or continuous wave 3 MHz ultrasound at either 0.2 or 0.4 W[Formula: see text]cm2, SATP/SATA. Ultrasound was applied to biceps muscles for 5 minutes on days 1 to 3. Muscle soreness, tenderness, and stiffness were measured pre-DOMS induction and at 24, 48, and 72 hours post-induction. Results: Pulsed ultrasound, 20% duty cycle, at 0.6 W[Formula: see text]cm2, SATP, (0.12 W[Formula: see text]cm2, SATA) reduced muscle soreness in a low-DOMS but not in a high-DOMS protocol. Continuous wave ultrasound at 0.4 W[Formula: see text]cm2, SATP/SATA reduced tenderness. Continuous ultrasound at 0.2 W[Formula: see text]cm2, SATP/SATA was marginally effective on stiffness and tenderness. Conclusion: The results have implications for ultrasound management of acute soft tissue injury and the use of DOMS as an experimental model for soft tissue inflammation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".