Effect of abatacept treatment on serum osteoclast-related biomarkers in patients with rheumatoid arthritis (RA)
Bibliographic record
Abstract
ABSTRACT: We evaluated the effect of abatacept treatment on osteoclast-related biomarkers and explored whether the biomarkers are associated with the therapeutic response in rheumatoid arthritis (RA) patients treated with abatacept.We enrolled 44 RA patients treated with abatacept from a multicenter prospective ultrasound cohort study of patients who received biologic or targeted synthetic disease-modifying antirheumatic drug therapy. We evaluated the disease activity score (DAS) 28-CRP (C-reactive protein), musculoskeletal ultrasound scores including the total grayscale score (GS)/power Doppler (PD) score and the serum concentrations of isoform 5b of tartrate-resistant acid phosphate (TRACP-5b) and soluble receptor activator of nuclear factor-κB ligand (sRANKL) at baseline and at 3 and 6 months of treatment. "PD responder" was defined as a patient whose Δtotal PD score over 6 months was greater than the median change of that.Abatacept significantly improved DAS28-CRP as well as the total GS/PD score over 6 months. Serum TRACP-5b was significantly elevated and serum sRANKL was significantly decreased at 6 months (P < .0001 and P < .01, respectively). At 6 months, serum sRANKL was significantly decreased in the patients who achieved DAS28-CRP remission and the PD responders but not in those who did not. However, serum TRACP-5b rose regardless of the therapeutic response.Among RA patients treated with abatacept, serum sRANKL decreased in the patients with a good therapeutic response, but serum TRACP-5b elevated paradoxically regardless of the therapeutic response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".