Bibliographic record
Abstract
Lateral epicondylitis is a painful condition related to the myotendinous origin of the extensor muscles at the lateral epicondyle of the humerus. Primary treatment typically involves the use of rest, non-steroidal anti-inflammatory drugs (NSAIDs), and physiotherapy. However, in refractory cases where conventional therapy is ineffective, ultrasound-guided injection therapies have become a growing form of treatment. These include needle tenotomy, autologous whole blood injection (AWB), platelet-rich plasma (PRP) injection and steroid injection. The consensus regarding the efficacy of individual approaches of ultrasound-guided treatment is unclear in the literature, and is explored further in this review.When evaluating these injection therapies individually, there are multiple case series describing the efficacy of each intervention in refractory lateral epicondylitis. A systematic review of needle tenotomy demonstrates an improvement in pain symptoms for patients with this condition, but all studies were poorly designed with no placebo or control group. Additionally, for PRP therapy, a systematic review performed in 2013 demonstrated a statistically significant improvement in pain and functionality for refractory lateral epicondylitis. However, these studies were similarly associated with a high risk of bias. Autologous whole blood injection has been evaluated through well-designed studies to show statistically significant reductions in pain with this intervention. But very few studies in total have been completed using AWB for lateral epicondylitis, and therefore no clear conclusions can be drawn at this time. Finally, corticosteroid use overall is unsupported in the evidence both in the short and long term, especially given that this condition is not an inflammatory pathology.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".