Personalizing Treatment Targets in Rheumatoid Arthritis by Using a Simple Prediction Model
Bibliographic record
Abstract
OBJECTIVE: To develop a personalized treatment target approach in patients with rheumatoid arthritis (RA) based on baseline risk factors for joint damage progression in combination with disease activity over time. METHODS: Data were used from the Nijmegen early RA cohort. Presence or absence of anticyclic citrullinated peptide antibodies (anti-CCP), high erythrocyte sedimentation rate, and erosions were translated into 4 risk profiles: 0, 1, 2, and 3. Joint damage progression was assessed with the Ratingen score, and disease activity with the original Disease Activity Score (DAS) over 3 years. The probability for joint damage progression was calculated for each risk profile and each DAS category using logistic regression models. The probabilities were translated into personalized disease activity treatment targets. RESULTS: More risk factors at baseline as well as a higher DAS level resulted in a higher probability for joint damage progression in a dose-dependent way. Low DAS corresponded with a probability of 0.0, 0.08, 0.20, and 0.58 in patients with 0, 1, 2, and 3 risk factors, respectively. Moderate DAS corresponded with a probability of 0.06 in patients with 0 risk factors and 0.35 with 1 risk factor. High DAS resulted in a probability of 0.50 with no risk factors present at baseline. CONCLUSION: Presence of anti-CCP, acute-phase response, and erosions at baseline can be used to set individual treatment targets in RA. In patients without these risk factors, a moderate DAS as a target is sufficient, while for patients with all 3 risk factors, a low DAS is not strict enough to limit the risk for joint damage.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".