Presence of Neuropathic-Like Symptoms in Individuals With Painful Tendinopathy/Overuse Injuries
Bibliographic record
Abstract
OBJECTIVE: This meta-analysis evaluated the prevalence of neuropathic-like symptoms in individuals with painful tendinopathies/overuse injuries. METHODS: Electronic literature searches on MEDLINE, CINAHL, PubMed, SCOPUS, and Web of Science databases were conducted up to January 20th, 2025. Studies reporting the prevalence of neuropathic-like symptoms in painful tendinopathy/overuse injury were included. The methodological quality was assessed with the Newcastle-Ottawa Quality Assessment Scale in cohort/case-control studies or the Physiotherapy Evidence Database (PEDro) in clinical trials. Random-effects models were used for meta-analytical pooled prevalence of neuropathic-like symptoms. RESULTS: From 1285 studies identified, 8 (1 case-control, 5 cohorts, and 2 clinical trials) met inclusion criteria. The sample included 920 participants with painful tendinopathy/overuse injury (47.6% female, age: 51, SD: 12.5 y). All studies included self-reported questionnaires for evaluating neuropathic-like symptomatology. The methodological quality was moderate-high. The overall prevalence of neuropathic-like symptoms in painful tendinopathies was 30% (95% CI: 22%-38%, n=8, I2 =79%). The prevalence by each tendinopathy was: plantar heel pain (44%, 95% CI: 17%-75%, n=2, I2 =96%), lateral epicondylalgia (42%, 95% CI: 30%-56%, n=2, I2 =30%), insertional Achilles tendinopathy (38%, 95% CI: 20%-60%, n=2, I2 = 71%), greater trochanteric pain syndrome (32%, 95% CI: 26%-39%, n=2, I2 =0%), patellar-quadricipital tendinopathy (16%, 95% CI: 5%-41%, n=3, I2 =29%), noninsertional Achilles tendinopathy (11%, 95% CI: 2%-41%, n=5, I2 =86%). CONCLUSION: The results of this meta-analysis suggests the presence of neuropathic-like symptoms in 30% of participants with painful tendinopathy, although this prevalence rate depends on specific condition. Identification of neuropathic symptoms in musculoskeletal pain conditions is important for diagnosis as it impacts its management.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.049 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".