Evidence‐Based Indications for Elbow Arthroscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it