The stiff elbow: Current concepts
Bibliographic record
Abstract
Elbow stiffness is defined as any loss of movement that is greater than 30° in extension and less than 120° in flexion. Causes of elbow stiffness can be classified as traumatic or atraumatic and as congenital or acquired. Any alteration affecting the stability elements of the elbow can lead to a reduction in the arc of movement. The classification is based on the specific structures involved (Kay's classification), anatomical location (Morrey's classification), or on the degree of severity of rigidity (Vidal's classification). Diagnosis is the result of a combination of medical history, physical examination (evaluating both active and passive movements), and imaging. The loss of soft tissue elasticity could be the result of bleeding, edema, granulation tissue formation, and fibrosis. Preventive measures include immobilization in extension, use of post-surgical drain, elastic compression bandage and continuous passive motion. Conservative treatment is used when elbow stiffness has been present for less than six months and consists of the use of serial casts, static or dynamic splints, CPM, physical therapy, manipulations and functional re-education. If conservative treatment fails or is not indicated, surgery is performed. Extrinsic rigidity cases are usually managed with an open or arthroscopic release, while those that are due to intrinsic causes can be managed with arthroplasties. The elbow is a joint that is particularly prone to developing stiffness due to its anatomical and biomechanical complexity, therefore the treatment of this pathology represents a challenge for the physiotherapist and the surgeon alike.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".