Determining <i>in vivo</i> sternoclavicular, acromioclavicular and glenohumeral joint centre locations from skin markers, CT-scans and intracortical pins: A comparison study
Bibliographic record
Abstract
To describe shoulder motion the sternoclavicular, acromioclavicular and glenohumeral joint centres must be accurately located. Within the literature various methods to estimate joint centres of rotation location are proposed, with no agreement of the method best suited to the shoulder. The objective of this study was to determine the most reliable non-invasive method for locating joint centre locations of the shoulder complex. Functional methods using pin mounted markers were compared to anatomical methods, functional methods using skin mounted markers, imaging-based methods using CT-scan data, and regression equations. Three participants took part in the study, that involved insertion of intracortical pins into the clavicle, scapula and humerus, a CT-scan of the shoulder, and finally data collection using a motion analysis system. The various methods to estimate joint centre location did not all agree, however suggestions about the most reliable non-invasive methods could be made. For the sternoclavicular joint, the authors suggest the anatomical method using the most ventral landmark on the sternoclavicular joint, as recommended by the International Society of Biomechanics. For the acromioclavicular joint, the authors suggest the anatomical method using the landmark defined as the most dorsal point on the acromioclavicular joint, as proposed by van der Helm. For the glenohumeral joint, the simple regression equation of Rab is recommended.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".