Muscle Activation in Specific Regions of the Trapezius During Modified Kendall Manual Muscle Tests
Bibliographic record
Abstract
CONTEXT: Manual muscle tests (MMTs) are often used when assessing shoulder injuries. For the trapezius, individual MMTs are used to selectively test the upper trapezius region (UTR), middle trapezius region (MTR), and lower trapezius region (LTR). The MMTs for each region are assumed to preferentially recruit the corresponding muscle fibers and produce a maximal contraction; however, whether this is true is unknown. OBJECTIVE: To determine if maximal voluntary isometric contractions (MVICs) for the upper trapezius (UT-MVIC), middle trapezius (MT-MVIC), and lower trapezius (LT-MVIC), adapted from the Kendall MMTs, recruited the corresponding trapezius regions. DESIGN: Crossover study. SETTING: Laboratory. PATIENTS OR OTHER PARTICIPANTS: A total of young, healthy individuals (10 men, 9 women, 1 not listed; age = 23.9 ± 1.7 years, height = 171.4 ± 9.6 cm, mass = 75.7 ± 11.6 kg). INTERVENTION(S): Participants performed 3 repetitions of each MVIC. High-density surface electromyography measurements were collected from the UTR, MTR, and LTR. MAIN OUTCOME MEASURE(S): Root mean square (excitation) of the UTR, MTR, and LTR. RESULTS: We observed an increase in UTR excitation during the LT-MVIC compared with the UT-MVIC (P = .016) and MT-MVIC (P < .001). The MTR excitation increased during the MT-MVIC (P = .001) and the LT-MVIC (P < .001) compared with the UT-MVIC. We also noted an increase in MTR excitation during the LT-MVIC compared with the MT-MVIC (P < .001). The LTR excitation increased during the MT-MVIC and LT-MVIC (P values < .001) compared with the UT-MVIC. CONCLUSIONS: The UT-MVIC and MT-MVIC did not necessarily recruit the corresponding trapezius regions more than the other MVICs did. Rather, the LT-MVIC appeared to produce the greatest excitation of all trapezius regions. Additional research is needed; however, clinicians should be aware that maximal contractions may not always recruit the desired muscle region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".