Position statement: nonoperative management of lateral epicondylitis in adults
Bibliographic record
Abstract
We sought to compare methods of nonsurgical treatment of lateral epicondylitis in men and women older than 18 years to develop a guideline intended for orthopedic surgeons and other health care providers who assess, counsel and care for these patients. We searched Medline, Embase and Cochrane through to Mar. 9, 2021, and included all English-language studies comparing nonsurgical approaches. We compared physiotherapy versus no active treatment, corticosteroids versus placebo, platelet-rich plasma (PRP) versus placebo, and autologous blood injection versus placebo. Outcomes of interest were pain outcomes (visual analogue scale scores) and functional outcomes. We rated the quality of the evidence and strength of recommendations using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. This guideline will benefit patients seeking nonsurgical intervention for lateral epicondylitis by improving counselling on nonsurgical treatment options and possible outcomes. It will also benefit surgical providers by improving their knowledge of various nonsurgical approaches. Data presented could be used to develop frameworks and tools for shared decision-making.
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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".