Predicting Remission Remains a Challenge in Patients with Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
The consequences of persistent active disease in juvenile idiopathic arthritis (JIA) include chronic pain and disability, in addition to growth disturbances and joint damage1,2. The last 2 decades have seen the development and licensing of biological therapies for JIA, revolutionizing patient care3. Now, more than ever, resolution of the signs and symptoms of JIA (i.e., remission) may be an attainable goal. However, even in cohorts of children and young people (CYP) with JIA where these newer therapies are widely available, fewer than 50% of CYP achieve remission in the first 10 years following diagnosis4. For outcomes to improve in JIA, clinicians must take advantage of the window of opportunity. This window represents a short time after diagnosis whereby early treatments may be most effective5. Thus, appropriate therapies must be used as early as possible. There is an ongoing push toward stratified or personalized medicine across specialties, including rheumatology6. If nonremission could be predicted, patients at higher risk could be managed differently, for example, with the earlier use of targeted therapies such as biologics. This would additionally minimize the risk of adverse events from exposure to unnecessary therapies that may be less successful at controlling disease in that patient. However, it is currently unclear which patients are predisposed toward a remission-like … Address correspondence to Prof. K.L. Hyrich, 2.800 Stopford Building, The University of Manchester, Oxford Road, Manchester M13 9PT, UK. E-mail: Kimme.hyrich{at}manchester.ac.uk
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".