Humeral head translation during glenohumeral abduction following computer-assisted shoulder hemiarthroplasty
Bibliographic record
Abstract
This study compared the effect of a computer-assisted and a traditional surgical technique on the kinematics of the glenohumeral joint during passive abduction after hemiarthroplasty of the shoulder for the treatment of fractures. We used seven pairs of fresh-frozen cadaver shoulders to create simulated four-part fractures of the proximal humerus, which were then reconstructed with hemiarthroplasty and reattachment of the tuberosities. The specimens were randomised, so that one from each pair was repaired using the computer-assisted technique, whereas a traditional hemiarthroplasty without navigation was performed in the contralateral shoulder. Kinematic data were obtained using an electromagnetic tracking device. The traditional technique resulted in posterior and inferior translation of the humeral head. No statistical differences were observed before or after computer-assisted surgery. Although it requires further improvement, the computer-assisted approach appears to allow glenohumeral kinematics to more closely replicate those of the native joint, potentially improving the function of the shoulder and extending the longevity of the prosthesis.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".