Spondyloarthritis: Accelerating the patient journey from first symptoms to adequate treatment
Bibliographic record
Abstract
The global aim of this thesis was to accelerate the patient journey from first symptoms to adequate treatment in spondyloarthritis (SpA) by a) tailoring the diagnostic process in order to enable an earlier diagnosis and b) tailoring the treatment strategy to shorten the time from diagnosis to remission and to increase remission rates in patients with SpA. To investigate which markers could aid in making an earlier diagnosis, we investigated which clinical features characterize females with SpA (chapter 2) and which clinical and imaging features are specifically present in individuals with an increased risk of developing SpA (chapter 3). Very early (pre-clinical) aggressive treatment as well as add-on treatment targeting low residual disease activity could induce remission in higher number of patients. To investigate tailored treatment strategies, we investigated the willingness of individuals at risk to develop SpA to use preventive treatment, if this would become available (chapter 4), if initial treatment with TNFα inhibitors induces higher remission rates in patients with early disease (chapter 5), if remission is sustained after discontinuing TNFα inhibitors which were started as initial treatment (chapter 6), and the safety and efficacy of an non-drug add-on treatment for patients with residual disease activity (chapter 7).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".