ASAS Health Index: The “All in One” for Spondyloarthritis Evaluation?
Bibliographic record
Abstract
The assessment of multifaceted disease processes is a key element in the management of chronic inflammatory rheumatic diseases such as axial spondyloarthritis (axSpA), including ankylosing spondylitis (AS). The evaluation includes not only patients’ history and clinical symptoms but also the assessment of disease activity, function, structural damage, and comorbidities. The management of patients with axSpA is especially challenging in this regard, because this complex disease entity has a wide variability of clinical signs and symptoms1,2. Within the variable course of SpA, adding to the burden of the disease are axial involvement, peripheral arthritis, enthesitis, and extramusculoskeletal involvement in other organs such as the eye, the skin, and the gut. The most prominent health problems in addition to inflammatory back pain are spinal stiffness, mobility limitations, fatigue, and sleep problems that are associated with significant restrictions in activities of daily living in patients with axSpA3. The evaluation of the current state of health of a patient with axSpA includes the assessment of several aspects of the disease with a focus on disease activity, because the degree of inflammatory activity is the main driver of pain, stiffness, and radiographic progression4. Therefore, the reduction of disease activity is of major importance and a central target for intervention, with remission as the main objective of treat-to-target (T2T) strategies5. However, assessment of disease activity alone cannot sufficiently characterize the entire effect of the disease on the patient3. This is especially relevant when evaluating impairments in physical function and spinal mobility in patients with axSpA, because it has been clearly shown that associated limitations depend on both inflammatory and structural changes6. A variety of validated tools for the assessment of axSpA, evaluating different aspects of the disease, is available and frequently used … Address correspondence to Dr. U. Kiltz, Rheumazentrum Ruhrgebiet, Claudiusstr. 45, Herne, NRW 44649, Germany. Email: uta.kiltz{at}elisabethgruppe.de.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.021 | 0.010 |
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".