A multiparametric risk table for loss of clinical remission status in patients with rheumatoid arthritis: a STARTER study post-hoc analysis
Bibliographic record
Abstract
OBJECTIVE: This post-hoc analysis was carried out on data acquired in the longitudinal Sonographic Tenosynovitis Assessment in RheumaToid arthritis patiEnts in Remission (STARTER) study. Our primary aim was to determine the predictive clinical and musculoskeletal ultrasonographic (MSUS) features associated with disease flare in RA patients in clinical remission, while our secondary aim was to evaluate the probability of disease flare based on clinical and MSUS features. METHODS: We analysed data for a total of 389 RA patients in DAS28-defined remission. All patients underwent a MSUS examination according to the OMERACT guidelines. Logistic regression and results, presented as odds ratio and 95% CI, were used for the evaluation of the association between selected variables and disease flare. Significant clinical and MSUS features were incorporated into a risk table for predicting disease flare within at least 12 months of follow-up in patients with RA remission. RESULTS: Within 12 months, 137 (35%) RA patients experienced a disease flare. RA patients who experienced a flare disease differed from those with persistent remission in terms of ACPA positivity (75.9% vs 62.3%, respectively; P = 0.007), percentage of sustained clinical remission at baseline (44.1% vs 68.5%, respectively; P = 0.001) and synovium power Doppler signal presence (58.4% vs 33.3%, respectively; P < 0.001). Based on these results, these three features were considered in a predictive model of disease flare with an adjusted odds ratio of 3.064 (95% CI 1.728-5.432). Finally, a risk table was constructed including the three significant predictive factors of disease flare occurring within 12 months from the enrolment. CONCLUSION: An adaptive flare-prediction model tool, based on data available in outpatient settings, was developed as a multiparametric risk table. If confirmed by external validation, this tool might support the defining of therapeutic strategies in RA patients in DAS28-defined remission status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| 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) | 0.005 | 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".