Influence of Multiplanar Shoulder Position on Transverse-Plane Torque Generation Capacity
Bibliographic record
Abstract
This study evaluated the influence of three-dimensional shoulder positioning on internal (IR) and external (ER) rotator muscle strength. Ten young adults were tested at full IR, full ER, and three interpolated angles between these extremes, across two arm elevation positions (45° and 90° in the scapular plane). Maximal torque and optimal angles for IR and ER were determined using second-order polynomial regression. We hypothesized that the optimal angles for IR and ER torque production do not coincide, and that the ERlIR torque ratio peaks at the optimal ER angle and reaches its minimum at the optimal IR angle in both positions. Additionally, we expected that arm elevation would influence torque ratios. The results showed a 30° difference between the optimal IR and ER angles, with significant differences in ERlIR ratios (p = 0.012) exclusively in the 45° elevation position. However, no significant differences in ERlIR ratios were found between the optimal IR angle and full ER, or between the optimal ER angle and full IR (p > 0.315) in either position. IR torque at the optimal IR angle was significantly higher in the 45° elevation position (median = 34.3 Nm) compared to the 90° elevation position (median = 31.3 Nm; p = 0.027). Despite this, arm elevation in the scapular plane did not significantly affect ERlIR ratios at any tested angle (p > 0.105). These findings suggest that ERlIR torque ratios are relatively stable across different shoulder configurations and are largely independent of arm elevation.
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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) | 0.002 | 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".