Variability in musculoskeletal fatigue responses associated with repeated exposure to an occupational overhead drilling task completed on successive days
Bibliographic record
Abstract
Emerging research suggests that muscular and kinematic responses to overhead work display a high degree of variability in fatigue-related muscular and kinematics changes, both between and within individuals when evaluated across separate days. This study examined whether electromyographic (EMG), kinematic, and kinetic responses to an overhead drilling task performed until volitional fatigue were comparable to those of a repeated identical exposure of the task completed 1 week later. Surface EMG and intramuscular EMG, sampled from 7 shoulder muscles, and right upper limb kinematics and kinetics were analyzed from 15 male and 14 female participants. No significant day-to-day changes in EMG mean power frequency (MPF) were observed, though serratus anterior displayed significantly less fatigue-related increase in EMG root-mean-squared (RMS) signal amplitude on day 2. Unfatigued upper kinematics on day 2 featured an increase in thoracohumeral elevation, elbow flexion, and decrease in wrist ulnar deviation compared to unfatigued state on day 1. Fatigue-related changes in shoulder joint flexion moment that were present on day 1 were reduced on day 2, suggesting that a more efficient overhead work strategy was learned and preserved across successive days. Day-to-day changes in upper limb joint angle variability, quantified by median absolute deviation (MdAD), were joint dependent. Despite yielding a variable fatigue-related kinetic strategy on both days, kinematic and kinetic fatigue-related changes on a second day of completing an overhead drilling task suggested a potential kinematic learning effect.
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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 | 0.000 | 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) | 0.001 | 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".