Is adiposity an under‐recognized risk factor for tendinopathy? A systematic review
Bibliographic record
Abstract
OBJECTIVE: Tendon injuries have been reported to occur more frequently in individuals with increased adiposity. Treatment also appears to have poorer outcomes among these individuals. Our objective was to examine the extent and consistency of associations between adiposity and tendinopathy. METHODS: A systematic review of observational studies was conducted. Eight electronic databases were searched (Allied and Complementary Medicine, Biological Abstracts, CINAHL, Current Contents, EMBase, Medline, SPORTDiscus, and Web of Science) and citation tracking was performed on included reports. Studies were included if they compared adiposity between subjects with and without tendon injury or examined adiposity as a predictor of conservative treatment success. RESULTS: Four longitudinal cohorts, 14 cross-sectional studies, 8 case-control studies, and 2 interventional studies (28 in total) met the inclusion criteria, providing a total of 19,949 individuals. Forty-two subpopulations were identified, 18 of which showed elevated adiposity to be associated with tendon injury (43%). Sensitivity analyses indicated a clustering of positive findings among studies that included clinical patients (81% positive) and among case-control studies (77% positive). CONCLUSION: Elevated adiposity is frequently associated with tendon injury. Published reports suggest that elevated adiposity is a risk factor for tendon injury, although this association appears to vary depending on aspects of study design and measurement. Adiposity is of particular interest in tendon research because, unlike a number of other reported risk factors for tendon injury, it is somewhat preventable and modifiable. Further research is required to determine if reducing adiposity will reduce the risk of tendon injury or improve the results of treatment.
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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".