Outcome Evaluation in Tendinopathy: Foundations of Assessment and a Summary of Selected Measures
Bibliographic record
Abstract
Synopsis Clinical measurement studies that address outcome evaluation for patients with tendinopathy should consider conceptual, clinical, practical, and measurement issues to guide the selection of valid measures. Clinical outcomes reported in research studies can provide benchmarks that assist with interpretation of scores during clinical decision making. Given the pathophysiology and functional impacts of tendinopathy, there is a need for outcome measures that assess physical impairments, activity performance, and patient-reported symptoms and function. Tendinopathy-specific patient-reported outcome measures have been shown to be superior to more generic tools for some conditions, such as lateral epicondyle tendinopathy (Patient-Rated Tennis Elbow Evaluation) and Achilles tendinopathy (Victorian Institute of Sport Assessment-Achilles), whereas both generic shoulder outcome measures and disease-specific measures perform similarly in individuals with rotator cuff tendinopathy. A patient-reported outcome measure that captures pain and limitation in function should be fundamental to outcome evaluation in patients with tendinopathy. The current measurement literature does not yet provide comprehensive empirical data to define optimal outcome measures for all types of tendinopathy. This article reviews concepts, instruments, and measurement properties that should provide clinicians with a foundation for assessment of condition severity and treatment outcomes in patients with tendinopathy. J Orthop Sports Phys Ther 2015;45(11):950-964. Epub 15 Oct 2015. doi:10.2519/jospt.2015.6054.
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 | 0.043 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".