Strength after the arthroscopic Latarjet procedure: Are shoulder internal rotation, elbow flexion & supination strength decreased?
Bibliographic record
Abstract
Background The Latarjet procedure is an effective shoulder stabilizing surgery, however, the procedure results in an alteration of anatomy that may result in shoulder and elbow weakness. Thus, the purpose of this study was to assess post-operative shoulder and elbow strength after the Latarjet procedure. We hypothesized that shoulder and elbow strength are not affected after the procedure. Methods The study group consisted of patients that had undergone the arthroscopic Latarjet procedure. An isokinetic dynamometer was used to evaluate the strength of bilateral shoulder internal rotation, elbow flexion, forearm supination using peak torque (N/m), as well as grip strength (kilograms). Shoulder range of motion and the potential effects of hand dominance were further analysed. Results Nineteen patients with a mean age of 29 years and an average follow up of 47 months were included. Shoulder internal rotation strength, elbow flexion and forearm supination strength and grip strength were not significantly different when compared to the non-operative side ( p > 0.13). The range of shoulder external rotation was significantly reduced ( p < 0.001) on the Latarjet side. Conclusion The results from this study demonstrate no statistically significant differences in the strength of shoulder internal rotation, elbow flexion, forearm supination or grip strength despite the surgical alterations to the subscapularis and conjoint tendon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".