Using Ultrasound to Improve Diagnostic Confidence and Management of Psoriatic Disease: Highlights From the GRAPPA 2023 Ultrasound Workshop
Bibliographic record
Abstract
The sensitivity of ultrasound (US) to detect, characterize, and monitor the relevant pathologies of psoriatic arthritis (PsA), including synovitis, enthesitis, tenosynovitis, and dactylitis, has made it an attractive tool for informing clinical decisions. The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) US working group ran 2 sessions during the annual GRAPPA meeting held in July 2023 in Dublin, Ireland. During the first workshop, the group presented 2 topics, followed by a live demonstration and a group discussion. The 2 topics were (1) an overview of the Diagnostic Ultrasound Enthesitis Tool (DUET) enthesitis scoring methodology, and (2) small hand-held probes-will the promise deliver? The live demonstration that followed compared the performance of 2 hand-held US (HHUS) devices vs a console US machine in patients with PsA, and the interactive group discussion considered gaps in the literature and future research suggestions relating to HHUS and its application in psoriatic disease. During the second session, the US working group provided further updates regarding the GRAPPA US studies currently underway or recently completed.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| 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; 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".