Outcome analysis of ulnar shortening osteotomy for ulnar impaction syndrome
Bibliographic record
Abstract
P ain on the ulnar side of the wrist is common and can arise from acute trauma, chronic degeneration or overuse (1).The ulnar side of wrist has been called the "black box" of the wrist because of its complex anatomy, the extensive differential diagnosis and difficulty in treatment (1).Ulnar-sided wrist pain can be frustrating to manage for both the patient and the physician, especially because significant pain can be present without any radiographic cause (2).The differential diagnosis in patients with ulnar-sided wrist pain includes ulnar impaction syndrome (UIS), degenerative arthritis, triangular fibrocartilage complex (TFCC) pathology, carpal ligament tears and instability, extensor carpi ulnaris (ECU) and flexor carpi ulnaris tendinosis, and incongruity of the distal radioulnar joint (DRUJ) (3,4).The extensive range of etiologies of pain is associated with a broad age group.In the younger age groups, it is often associated with vocational injuries.A common cause of ulnar-sided wrist pain is UIS.This entity is believed to occur secondary to ulnar head compression against the TFCC and ulnar carpus, leading to degeneration of these structures (2,5).UIS is also known as ulnocarpal abutment syndrome and was described as early as 1941 by Henry Milch (3,6).The treatment of UIS includes nonoperative options such as intermittent immobilization, nonsteroidal anti-inflammatory drugs, avoidance of ulnar deviation and steroid injections (1).If this fails, there are currently three surgical options: the arthroscopic 'wafer' procedure, ulnar shortening osteotomy (USO) and hemiresection arthroplasty.The surgical treatments are based on the theory that shortening the ulna will decrease the load on the TFCC (3).Previous work has shown that an increase in the ulnar variance of only 2.5 mm can increase the axial load on the forearm by 40%, whereas a decrease of 2.5 mm can
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".