Scapular kinematic reconstruction – segmental optimization, multibody optimization with open-loop or closed-loop chains: which one should be preferred?
Bibliographic record
Abstract
Several numerical approaches have been developed to address the soft tissue artefact, such as the segmental optimization and multibody kinematics optimizations using either open-loop or closed-loop chains. However, it is still not clear which method is the most efficient for scapular kinematics reconstruction. In this study, six kinematic models were compared to a silver standard, i.e. a scapula palpator, during a series of 55 postures with maximal amplitudes of both the arm and scapula performed by 15 subjects. The most accurate approaches were the multibody optimization with a closed-loop chain and the segmental optimization. They provided averaged scapula misorientations of 14.9 ± 6.7° and 16 ± 7.1, respectively. Regarding the closed-loop chain integrating a point-to-ellipsoid scapulothoracic joint, the ellipsoid providing the most accurate results was a thorax-size ellipsoid fitting the area browsed by the scapula. Eventually, considering the high implementation costs of a multibody kinematics optimization, the segmental optimization could be considered as appropriate for scapular kinematics reconstruction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".