Efficacy of Intensive Physiotherapy in Combination with Low-dose Etanercept in Active Spondyloarthritis: A Monocentric Pilot Study
Bibliographic record
Abstract
To the Editor: The introduction of tumor necrosis factor-a inhibitors (TNFi) in the treatment of spondyloarthritis was a landmark1,2, but ongoing research in health economics still questions the cost-effectiveness of this therapy3. In contrast, nonpharmacological measures, such as physical therapy, appear to be more economical in this setting and have also shown a positive effect on the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and the Bath Ankylosing Spondylitis Functionality Index (BASFI), as well as on pain and mobility4. Although the idea of combining these 2 treatment modalities is attractive, there is still a lack of high-quality studies that examine the effects of physical therapy in combination with drugs such as TNFi, particularly in everyday clinical settings5,6. To address this idea, we performed a proof-of-concept study, in which the dose of the TNFi etanercept (ETN) was 50% of the standard dose for adult patients (i.e., 25 mg/week subcutaneously) and combined with standard intensive physiotherapy (3 sessions of 30 min/week; … Address correspondence to Professor Uwe Lange, Department of Internal Medicine and Rheumatology, Justus-Liebig-University Giessen, Kerckhoff Clinic Bad Nauheim, Benekestr. 2-8, Bad Nauheim 61231, Germany. E-mail: u.lange{at}kerckhoff-klinik.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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".