Upper Extremity Surgeon's Guide to the Evaluation of the Shoulder Girdle and Diagnosis of Associated Pathology
Bibliographic record
Abstract
Shoulder injuries can pose as a diagnostic challenge for clinicians. Owing to the wide variety of pathologies and the fact that they frequently coexist, diagnosis can be difficult. Accurate diagnosis and management not only require a thorough understanding of each condition but the ability to narrow down the broad differential through detailed physical examination. This review article provides an in-depth examination of common shoulder conditions and outlines key physical examination techniques for upper extremity surgeons and other clinicians who manage these conditions. It begins by providing an up-to-date overview of the various pathologies that affect the shoulder girdle, including rotator cuff tears, quadrangular space syndrome, labral instability and glenohumeral bone loss, adhesive capsulitis, superior labral and biceps pathology, thoracic outlet syndrome, acromioclavicular joint degeneration, and glenohumeral osteoarthritis. This review then discusses key physical examination aspects, such as the history of present illness, cervical spine evaluation, range of motion assessment, strength testing, and shoulder girdle-specific maneuvers. This comprehensive review highlights the importance of a thorough understanding of shoulder physical examination and special diagnostic tests, emphasizing history-taking and examination to ensure diagnostic accuracy and optimize patient outcomes for surgeons.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.106 | 0.104 |
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".