Explaining Variability in the Prevalence of Achilles Tendon Abnormalities: A Systematic Review With Meta-analysis of Imaging Studies in Asymptomatic Individuals
Bibliographic record
Abstract
Objective To estimate the prevalence of, and factors associated with, Achilles tendon abnormalities observed on imaging in asymptomatic individuals. Design Systematic review with stratified meta-analysis and meta-regression. Literature Search Embase, Scopus, MEDLINE, CINAHL, SPORTDiscus, and Web of Science were searched from 1980 to August 2020. Study Selection Criteria We included studies that reported the prevalence of Achilles tendon abnormalities, observed with any imaging modality, in an asymptomatic population. We excluded studies if participant mean age was younger than 12 years or if participants had current/previous lower-limb tendon injuries/symptoms or other systemic conditions. Data Synthesis Random-effects proportion meta-analysis was used to estimate prevalence. We used meta-regression for continuous variables (mean age and body mass index [BMI], sample size, proportion of female participants) and stratified categorical variables (imaging modality and participation in physical activity) to explain between-study heterogeneity. Results We included 91 studies (10 156 limbs, 5841 participants). The prevalence of Achilles tendon abnormalities on imaging ranged from 0% to 80% per participant. Between-study heterogeneity was high (I 2 >90%, P<.001), precluding data pooling. Between-study heterogeneity was partly explained by participant mean BMI (slope, 2.8% per 1-unit increase in BMI; 95% confidence interval: 0.57%, 5.03%; P = .015) and participation in physical activity per limb, and mean age of 40 years old or older (P = .022) per participant. Conclusion There was substantial variability in the prevalence of Achilles tendon abnormalities on imaging in asymptomatic individuals. Higher prevalence of abnormalities was associated with older age (40 years old or older), higher BMI, and participation in physical activity. A large proportion of heterogeneity remains unaccounted for, likely due to variations in abnormality definitions and study design. J Orthop Sports Phys Ther 2021;51(5):232–252. Epub 28 Mar 2021. doi:10.2519/jospt.2021.9970
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".