Operative treatment of ulnar impaction syndrome: a systematic review
Bibliographic record
Abstract
This article critically and systematically reviews the surgical treatments for ulnar impaction syndrome. Three types of treatments currently exist: arthroscopic wafer procedure, open wafer procedure, and ulna shortening osteotomy. A total of 36 articles were included from searching the electronic databases PubMed MEDLINE, Ovid MEDLINE, and Ovid EMBASE. Studies were evaluated for quality using the Modified Detsky Score. Of these, 14 articles had a Modified Detsky Score of 6/10 or higher. Satisfaction rates were 100% for arthroscopic wafer procedure, 89% for open wafer procedure, and 84% for ulna shortening osteotomy. The percentage of participants reporting an excellent or good outcome was 82% for arthroscopic wafer procedure, 87% for open wafer procedure, and 76% for ulna shortening osteotomy. In conclusion, available evidence shows that arthroscopic wafer procedure and open wafer procedure may be viable alternatives to the more popular ulna shortening osteotomy, but clinical superiority is yet to be established. Future research should focus on prospective cohort methods and should report participant outcomes using validated scoring methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".