A Review of Common Shoulder Injuries
Bibliographic record
Abstract
The shoulder complex is an intricate combination of bones, muscles, and ligaments that function synergistically to move the arm. While the shoulder is a very mobile joint, allowing for movement in all planes, it is not an apparatus known for stability. The injuries that can be sustained by the shoulder are often extensive and could give rise to further injuries in other aspects of the body, including the arm, back, and sternum. Two of the most common injuries that can be sustained by the shoulder include clavicular fractures and anterior shoulder dislocations. Clavicular fractures are most commonly sustained by direct compressive force directed towards the sternum and applied to the ipsilateral shoulder, while anterior dislocations commonly occur as a result of direct force projected anteriorly while the arm is externally rotated and abducted. The mechanism of injury for both clavicular fractures and anterior dislocations dictates the injuries' severity which subsequently determines the extent of treatment and rehabilitation that is needed. Both conservative and surgical methods are effective in treating shoulder injuries depending upon an individual's activity level and the extent of the injury. Following treatment, proper rehabilitation of the injury is crucial to regain the shoulder's active pain-free range of motion, strength of surrounding muscles, and neuromuscular control, while ensuring a timely return to daily activities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".