The OMERACT Stepwise Approach to Select and Develop Imaging Outcome Measurement Instruments: The Musculoskeletal Ultrasound Example
Bibliographic record
Abstract
OBJECTIVE: To describe the Outcome Measures in Rheumatology (OMERACT) stepwise approach to select and develop an imaging instrument with musculoskeletal ultrasound (US) as an example. METHODS: The OMERACT US Working Group (WG) developed a 4-step process to select instruments based on imaging. Step 1 applies the OMERACT Framework Instrument Selection Algorithm (OFISA) to existing US outcome measurement instruments for a specific indication. This step requires a literature review focused on the truth, discrimination, and feasibility aspects of the instrument for the target pathology. When the evidence is completely unsatisfactory, Step 2 is a consensus process to define the US characteristics of the target pathology including one or more so-called "elementary lesions". Step 3 applies the agreed definitions to the image, evaluates their reliability, develops a severity grading of the lesion(s) at a given anatomical site, and evaluates the effect of the acquisition technique on feasibility and lesion(s) detection. Step 4 applies and assesses the definition(s) and scoring system(s) in cross-sectional studies and multicenter trials. The imaging instrument is now ready to pass a final OFISA check. RESULTS: With this process in place, the US WG now has 18 subgroups developing US instruments in 10 different diseases. Half of them have passed Step 3, and the groups for enthesitis (spondyloarthritis, psoriatic arthritis), synovitis, and tenosynovitis (rheumatoid arthritis) have finished Step 4. CONCLUSION: The US WG approach to select and develop outcome measurement instruments based on imaging has been repeatedly and successfully applied in US, but is generic for imaging and fits with OMERACT Filter 2.1.
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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 | 0.108 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".