10 Recommended core outcome domains for tendinopathy derived from a delphi of patients and health care professionals: the groningen ISTS2018 consensus
Bibliographic record
Abstract
Introduction Tendinopathy (local tendon pain associated with physical activity) is a challenge to treat despite recent advances. One factor contributing to this challenge is our limited ability to synthesise/meta-analyse research findings, which is further compounded by a lack of valid outcome measures. We determined the core outcome domains against which outcome measures could be recommended. Materials and methods We conducted a Delphi study of patients and health care professionals (HCP) in two parts: an online survey and consensus meeting. Online survey items were extracted from clinical trial reports. Agree, disagree, or unsure were options in response to: ‘The ‘item’ is important enough to be included in a core domain set of tendinopathy’. A-priori criterion of ≥70% participant agreement was deemed for selection of a core domain. Results 32 patients and 28 HCP (92% had >10 years of tendinopathy experience, 71% consulted >10 cases per month) completed the online survey. 2 patients and 15 HCP attended the consensus meeting. Of the original 24 items (from trial reports); 9 were core: Patient overall rating, participation, pain on activity/loading, disability, function, physical function capacity, quality of life, psychology, and pain over a specified timeframe. Eight items were not core domains: range of motion, palpation, clinical examination, structure, pain on examination or without other specification, drop out, and sensory modality pain. Remaining seven items did not meet criterion. Conclusion The core domain set serves as a guide for reporting of outcomes in clinical trials. Further research should determine these outcomes for each specific tendon.
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 | 0.304 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".