Range of Motion as a Predictor of Clinical Shoulder Pain During Recovery From Delayed-Onset Muscle Soreness
Bibliographic record
Abstract
CONTEXT: Athletic trainers use clinical pain and range of motion (ROM) to gauge recovery after musculoskeletal injury. Limited evidence to date suggests which shoulder ROM measures can predict symptomatic relief and functional recovery after delayed-onset muscle soreness (DOMS). OBJECTIVE: To determine whether shoulder passive internal rotation, passive external rotation, active abduction, and active flexion and evoked pain with abduction are associated with resting pain experienced after exercise-induced DOMS. DESIGN: Descriptive laboratory study. SETTING: Controlled research laboratory. PATIENTS OR OTHER PARTICIPANTS: A total of 110 healthy, right-hand-dominant participants (44 men: age = 25.39 ± 7.00 years, height = 178.93 ± 7.01 cm, weight = 78.59 ± 14.04 kg; 66 women: age = 22.98 ± 6.11 years, height = 164.64 ± 6.94 cm, weight = 61.86 ± 11.67 kg). INTERVENTION(S): Participants completed an exercise-induced DOMS protocol for the external rotators of the dominant shoulder to replicate muscle injury. MAIN OUTCOME MEASURE(S): Current resting pain was assessed daily for 96 hours using the Brief Pain Inventory. We evaluated functional recovery with measures of ROM in abduction, internal rotation, external rotation, and flexion. Evoked pain with active abduction was reported, and the pain rating served as the dependent variable in the regression model. RESULTS: Impairment measures explained resting pain at 48 (R2 = 0.392) and 96 hours (R2 = 0.164). Abduction and internal-rotation ROM and evoked pain with abduction predicted resting pain at 48 hours (P < .001). At 96 hours, evoked pain with abduction of the injured arm (P < .001) was the significant contributor to resting pain. CONCLUSIONS: These models suggest that resting pain after experimentally induced DOMS occurs at 48 hours and is associated with specific ranges of motion and evoked pain with abduction.
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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 | 0.004 |
| 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) | 0.001 | 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".