4. The Natural History of Enthesitis-related Arthritis on Biologic Therapy
Bibliographic record
Abstract
Background: Magnetic resonance imaging (MRI) can provide a wealth of information about inflammation, erosions, fusion and fat metaplasia in the sacroiliac joints (SIJs) of patients with enthesitis-related arthritis (ERA). However, there is currently a lack of information regarding the natural history of imaging features in ERA patients with axial disease who are treated with biologic therapy. For example, it is unclear whether biologic treatment prevents fusion of the sacroiliac joints, and the relationship between bone marrow oedema and fat metaplasia is uncertain. Aim: To evaluate the effect of treatment on imaging features in young patients with ERA receiving biologic therapy. Methods: A picture archiving and communication system (PACS) search was used to identify all adolescent and young adult patients aged 12-24 with ERA who had undergone at least three MRI scans of the SIJs, over at least a two-year period, with scans before and after anti-TNF treatment. For each scan, inflammation severity was scored on short tau inversion recovery (STIR) images using the Spondyloarthritis Research Consortium of Canada scoring system1. Additionally, structural features (specifically erosions, fat metaplasia and fusion) were assessed using a recently proposed structural score2. The images were assessed by a consultant musculoskeletal radiologist with over 25 years of experience in musculoskeletal MRI. Pre- and post-treatment scores were compared using a multilevel mixed-effects linear regression model, which accounted for clustering effects.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".