Excitation distribution of the trapezius changes in response to increasing contraction intensity, but not repeated contractions
Bibliographic record
Abstract
Upper trapezius (UT) excitation redistributes with experimentally-induced muscle pain, fatigue, and repeated contractions. Excitation distribution variability is proposed to reduce the likelihood of shoulder pain and pathology by reducing cumulative stress on musculoskeletal structures. While the middle (MT) and lower (LT) trapezius are pivotal in scapular stabilization, it remains unclear whether they display similar excitation distribution variability with repeated or increasing contraction intensity. We determined if excitation distribution of the UT, MT, and LT differ: 1) during isometric contractions at different intensities (30 % and 60 % of maximum voluntary isometric contraction (MVIC)); and 2) with repeated contractions at 60 % MVIC. Nineteen individuals completed MVICs and submaximal contractions for the UT, MT, and LT while high-density electromyography was collected. Statistical parametric mapping t-tests were performed between intensities and the 1st and 5th repetition at 60 % MVIC. UT, MT, and LT excitation distribution changed with increasing contraction intensity in 358 (∼92 % of the map), 54 (∼14 %), and 270 pixels (∼70 %), respectively. No pixels exceeded significance with repeated contractions for any muscle. Barycentre analyses revealed no significant results. These results suggest that regions of the trapezius muscle use different neuromuscular strategies in response to changes in contraction intensity and repeated contractions.
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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) | 0.003 | 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".