Spartacus: A review and aggregation of reference datasets reporting the normal shoulder girdle kinematics during uniplanar humerus motions
Bibliographic record
Abstract
Understanding 3D shoulder girdle kinematics is essential for applications in medicine, sports, and computational biomechanics, where reference data help quantify deviations from the standard. Unfortunately, existing datasets on shoulder kinematics based on gold-standard measurements are often inconsistent in data collection methods, segment definitions, and joint kinematics computation, limiting their relevance. This study reviews and aggregates available datasets to establish Spartacus, a comprehensive reference for normal shoulder girdle kinematics. We gathered 20 datasets, published between 2000 and 2023, and aligned them, when possible, with the International Society of Biomechanics recommendations to enable population-wide analysis. Spartacus comprises data recorded in vivo (70 %) or ex vivo (30 %) on 245 shoulders using intracortical pins (55 %) or imaging (45 %), primarily during active dynamic (65 %) movements. The dataset predominantly covers arm elevation records in the scapular (70 %), frontal (50 %), and sagittal (50 %) planes, but also includes internal-external rotation and horizontal flexion. Both rotations and translations are represented across all shoulder girdle joints, with scapulothoracic rotations being the most frequent (75 %). Our approach corrects or at least compensates for six identified deviations in local coordinate system definitions and joint kinematics computation methods. Substantial inconsistencies across existing datasets reveal a need for improved standardisation to facilitate reliable data comparisons. Spartacus, openly available, will enable researchers to explore normal shoulder girdle kinematics, and provides a foundation for future clinical and biomechanical studies.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".