Outcome Domains and Measures in Total Joint Replacement Clinical Trials: Can We Harmonize Them? An OMERACT Collaborative Initiative
Bibliographic record
Abstract
OBJECTIVE: To develop a plan for harmonizing outcomes for people undergoing total joint replacement (TJR), to achieve consensus regarding TJR outcome research. METHODS: The TJR working group met during the 2014 Outcome Measures in Rheumatology (OMERACT) 12 meeting in Budapest, Hungary. Multiple conference calls preceded the face-to-face meeting. Brief presentations were made during a 1.5-h meeting, which included an overview of published systematic reviews of TJR trials and the results of a recent systematic review of TJR clinical trial outcome domains and measures. This was followed by discussion of potential core set areas/domains for TJR clinical trials (as per OMERACT Filter 2.0) as well as the challenges associated with the measurement of these domains. RESULTS: Working group participants discussed which TJR clinical trial outcome domains/areas map to the inner versus outer core for core domain set. Several challenges were identified with TJR outcomes including how to best measure function after TJR, elucidating the source of the pre- and post-TJR joint pain being measured, joint-specific versus generic quality of life instruments and the importance of patient satisfaction and revision surgery as outcomes. A preliminary core domain set for TJR clinical trials was proposed and included pain, function, patient satisfaction, revision, adverse events, and death. This core domain set will be further vetted with a broader audience. CONCLUSION: An international effort with active collaboration with the orthopedic community to standardize key outcome domains and measures is under way with the TJR working group. This effort will be further developed with new collaborations.
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 | 0.858 | 0.802 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.015 | 0.036 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".