GRAPPA Debate: Be it Resolved That Clinical Enthesitis Indices Do Not Reflect True Enthesitis and Hence Should Be Discontinued
Bibliographic record
Abstract
Enthesitis is increasingly recognized as a key manifestation of psoriatic disease (PsD). However, how best to assess enthesitis remains a point of discussion due to limitations of the existing assessment tools, including their inability to differentiate between inflammatory and noninflammatory enthesitis as well as a high placebo response in clinical trials. At the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2024 annual meeting, a debate was held to address whether traditional clinical enthesitis indices should be discontinued for PsD. Dr. Sibel Aydin advocated for their discontinuation, emphasizing that clinical indices often capture "enthesalgia" (pain at the entheseal sites overlapping with pain disorders like fibromyalgia) rather than true inflammatory enthesitis. These clinical indices may lack specificity for detecting inflammation, which can lead to inaccurate assessments. Further, studies show high reports of placebo response when using clinical indices, suggesting their limitations in discriminating active disease from noninflammatory pain mechanisms. Aydin advocated for prioritizing emerging imaging tools over traditional clinical indices. Dr. Atul Deodhar argued against discontinuation of traditional clinical enthesitis indices, highlighting that despite limitations, these indices have been used successfully in multiple randomized controlled trials, leading to approval of numerous treatment options for psoriatic arthritis. Although promising, alternative imaging modalities like ultrasound to evaluate inflammation come with their own challenges, including operator dependency, variability in interpretation, and lack of regulatory approval as a standardized outcome measure. This report presents both perspectives, analyzing the evidence and implications for the future of enthesitis assessment in clinical practice.
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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 | 0.032 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.043 | 0.054 |
| Insufficient payload (model declined to judge) | 0.021 | 0.023 |
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".