Load-Induced Glenohumeral Translation After Rotator Cuff Tears: Protocol for an In Vivo Study
Bibliographic record
Abstract
BACKGROUND: Rotator cuff tears are a common shoulder injury, but they sometimes remain undiagnosed, as symptoms can be limited. Altered shoulder biomechanics can lead to secondary damage and degeneration. In biomechanical analyses, the shoulder (ie, the glenohumeral joint) is normally idealized as a ball-and-socket joint, even though a translation is often observed clinically. To date, no conclusive changes in glenohumeral translation have been reported in patients with rotator cuff tears, and it is unknown how an additional handheld weight that is comparable to those used during daily activities will affect glenohumeral translations in patients with rotator cuff tears. OBJECTIVE: This study aims to assess the load-induced glenohumeral translation (liTr) in patients with rotator cuff tears and its association with the load-induced changes in muscle activation (liMA). METHODS: Patients and asymptomatic controls will be recruited. Participants will fill out health questionnaires and perform 30° arm abduction and adduction trials, during which they will hold different handheld weights of a maximum of 4 kg while motion capture and electromyographic data are collected. In addition, fluoroscopic images of the shoulders will be taken for the same movements. Isometric shoulder muscle strength for abduction and rotation will be assessed with a dynamometer. Finally, shoulder magnetic resonance images will be acquired to assess muscle status and injury presence. The dose-response relationship between additional weight, liTr, and liMA will be evaluated. RESULTS: Recruitment and data collection began in May 2021, and they will last until the recruitment target is achieved. Data collection is expected to be completed by the end of 2022. As of November 2022, data processing and analysis are in progress, and the first results are expected to be submitted for publication in 2023. CONCLUSIONS: This study will aid our understanding of biological variations in liTr, the influence of disease pathology on liTr, the potential compensation of rotator cuff tears by muscle activation and size, and the association between liTr and patient outcomes. The outcomes will be relevant for diagnosis, treatment, and rehabilitation planning in patients with rotator cuff tears. TRIAL REGISTRATION: ClinicalTrials.gov NCT04819724; https://clinicaltrials.gov/ct2/show/NCT04819724. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43769.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".