Extra-articular arthroscopic release of lateral epicondylitis: a prospective study
Bibliographic record
Abstract
Background Operative management of lateral epicondylitis can be managed with percutaneous, arthroscopic, or open surgical release. Extraarticular arthroscopic release is a new technique, and no study has compared its outcomes and risk profile. Methods A 26-patient cohort was reviewed before and after extraarticular arthroscopic release, which was performed by the senior author. The Mayo Elbow Performance Scores were used as a functional outcome score and obtained via a phone interview. Results were analyzed using a paired t-test with a statistical significance set at P < .05. Results Of the 26 patients, 10 were being treated under workers compensation. Preoperative Mayo Elbow Performance Score was 47.5, and the postoperative score was 90.2 with a significant difference of 42.7 ( P value = .05). The workers compensation group scored 13.3 points lower postoperatively than the remainder of patients, which was shown to also be significant with a P value of .002. Discussion and Conclusion The advantage of extraarticular arthroscopic release was better visualization of affected structures, which improved accuracy of debridement, and a small capsulotomy, which decreased the risk of a transient radial nerve palsy. Overall, extraarticular arthroscopic results were found to be good and comparable to the results of other operative techniques with the added advantage of a lower risk profile.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".