Bibliographic record
Abstract
This thesis explores advancements in monitoring and treatment strategies for Juvenile Idiopathic Arthritis (JIA), aiming to improve personalized care. In the first part, novel monitoring strategies are investigated, including the feasibility of capillary blood sampling at home as an alternative to venous blood draws at the hospital. Results provide insights into the feasibility and challenges associated with self-sampling. The presented laboratory studies provide evidence for the comparability between results from capillary and venous blood samples, supporting the potential for remote monitoring. In addition, a mobile eHealth application, electronic dashboard, and web-based surveys are examined in different studies, demonstrating that these technologies could provide insights in disease status and disease course, and could be used as monitoring tools, especially for patients with stable or inactive disease. In the second part, research is presented to further improve and tailor JIA treatment. Potential causes for bDMARD therapy failure, including the formation of anti-drug antibodies and low drug levels, are discussed. The thesis also examines biologic therapy withdrawal, showing that stopping specific bDMARDs in JIA patients with clinically inactive disease leads to significant cost reductions. Furthermore, the development of prediction models for methotrexate response is explored, which remains the first-line treatment agent for non-systemic JIA. Methodological concerns of currently available prediction models are highlighted, and new prediction models are developed. While these newly developed models demonstrate moderate performance, further refinement and validation are necessary. In conclusion, this thesis elaborates on current research challenges in the field of JIA and provides new evidence towards a data-driven and personalized approach to monitoring and treatment strategies. The integration of standardized data collection, digital solutions for JIA patients (including remote laboratory monitoring and eHealth applications), predictive models, cost-effective treatment adjustments, and analytical applications could further enable personalized treatment and monitoring while improving the quality of care for children and adolescents with JIA.
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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".