Validation of a Novel In-Vitro Simulator for Real-Time Control of Active Shoulder Movements in Various Planes of Motion
Bibliographic record
Abstract
In-vitro simulation of active shoulder joint motion is critical to gaining an understanding of the effects of surgical procedures and implant designs. However, development of systems for the accurate simulation of active shoulder motion has lagged well behind those implemented for the lower limb and elbow, which have used principles of closed-loop joint angle control 1,4. In contrast, active shoulder motion has been confined to simulators that can hold static joint angles through the application of loads based on computer model outputs 2, or that use constant velocity of the middle deltoid while using open-loop control to apportion other muscle loads as a function of a-priori physiologic loading ratios 3. Neither of these schemes utilizes real-time feedback of kinematic data in order to follow smooth, predefined profiles. The lack of more refined shoulder simulators, based on control theory, can primarily be attributed to the complexity of shoulder motion and the number of degrees of freedom (DOFs) ( i.e. plane of abduction, abduction angle, and axial rotation) which must be controlled.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".