Development of a core outcome set for lateral elbow tendinopathy (COS-LET) using best available evidence and an international consensus process
Bibliographic record
Abstract
OBJECTIVES: To develop a core outcome set for lateral elbow tendinopathy (COS-LET) and to provide guidance for outcome evaluation in future studies. METHODS: We implemented a multi-stage mixed-methods design combining two systematic reviews, domain mapping of outcome measurement instruments to the core domains of tendinopathy, psychometric analysis of instruments, two patient focus groups and a Delphi study incorporating two surveys and an international consensus meeting. Following the OMERACT guidelines, we used a 70% threshold for consensus. RESULTS: 38 clinicians/researchers and 9 patients participated. 60 instruments were assessed for inclusion. The only instrument that was recommended for the COS-LET was Patient Rated Tennis Elbow Evaluation (PRTEE) for the disability domain. Interim recommendations were made to use: the PRTEE function subscale for the function domain; PRTEE pain subscale items 1, 4 and 5 for the pain over a specified time domain; pain-free grip strength for the physical function capacity domain; a Numerical Rating Scale measuring pain on gripping for the pain on activity/loading domain; and time off work for the participation in life activities domain. No recommendations could be made for the quality-of-life, patient rating of condition and psychological factors domains. CONCLUSIONS: The COS-LET comprises the PRTEE for the disability domain. Interim-use recommendations included PRTEE subscales, time off work, pain-free grip strength and a Numerical Rating Scale measuring pain on gripping. Further work is required to validate these interim measures and develop suitable measures to capture the other domains.
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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 | 0.306 | 0.346 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.031 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".