Evidence‐Based Indications for Elbow Arthroscopy
Bibliographic record
Abstract
PURPOSE: The purpose was to review the literature on the outcomes of elbow arthroscopy and to make evidence-based recommendations for or against elbow arthroscopy for the treatment of various conditions. Our hypothesis was that the evidence would support the use of elbow arthroscopy in the management of common elbow conditions. METHODS: A literature search was performed by use of the PubMed database in October 2010. All therapeutic studies investigating the results of treatment with elbow arthroscopy were analyzed for outcomes and complications. The literature specific to common elbow arthroscopy indications was summarized and was assigned a grade of recommendation based on the available evidence. RESULTS: There is fair-quality evidence for elbow arthroscopy in the treatment of rheumatoid arthritis of the elbow and lateral epicondylitis (grade B recommendation). There is poor-quality evidence for, rather than against, the arthroscopic treatment of degenerative arthritis, osteochondritis dissecans, radial head resection, loose bodies, post-traumatic arthrofibrosis, posteromedial impingement, excision of a plica, and fractures of the capitellum, coronoid process, and radial head (grade C(f) recommendation). There is insufficient evidence to give a recommendation for or against the arthroscopic treatment of posterolateral rotatory instability and septic arthritis (grade I recommendation). CONCLUSIONS: The available evidence supports the use of elbow arthroscopy in the management of the majority of conditions where it is currently used. The quality of the evidence, however, is generally fair to poor. LEVEL OF EVIDENCE: Level IV, systematic review of Level II-IV studies.
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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 | 0.022 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| 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".