Optimizing repair of tendon ruptures and chronic tendinopathies: Integrating the use of biomarkers with biological interventions to improve patient outcomes and clinical trial design
Bibliographic record
Abstract
Tendons are dense connective tissues of the musculoskeletal system that link bones with muscles to foster mobility. They have complex structures and exist in varying biomechanical, metabolic and biological environments. In addition, tendon composition and mechanical properties can change over the lifespan as an individual ages. Many tendons function in high stress conditions with a low vascular and neuronal supply, conditions often leading to development of chronic tendinopathies, and in some cases, overt rupture of the tissues. Given their essential nature for human mobility and navigation through the environment, the effective repair and regeneration of different tendons after injury or damage is critical for quality of life, and for elite athletes, the return to sport participation at a high level. However, for mainly unknown reasons, the outcomes following injury are not always successful and lead to functional compromise and risk for re-injury. Thus, there is a need to identify those patients who are at risk for developing tendon problems, as well those at risk for poor outcomes after injury and to design interventions to improve outcomes after injury or rupture to specific tendons. This review will discuss recent advances in the identification of biomarkers prognostic for successful and less successful outcomes after tendon injury, and the mechanistic implications of such biomarkers, as well as the potential for specific biologic interventions to enhance outcomes to improve both quality of life and a return to participation in sports. In addition, the implication of these biomarkers for clinical trial design is discussed, as is the issue of whether such biomarkers for successful healing of one tendon can be extended to all tendons or are valid only for tendons in specific biomechanical and biological environments. As maintaining an active lifestyle is critical for health, the successful implementation of these advances will benefit the large number of individuals at risk.
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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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".