Tocilizumab Treatment Increases Left Ventricular Ejection Fraction and Decreases Left Ventricular Mass Index in Patients with Rheumatoid Arthritis without Cardiac Symptoms: Assessed Using 3.0 Tesla Cardiac Magnetic Resonance Imaging
Bibliographic record
Abstract
OBJECTIVE: The aim of our pilot study was to prospectively evaluate the effect of inhibiting interleukin 6 on the left ventricular (LV) structure and function in patients with rheumatoid arthritis (RA) without cardiac symptoms, using cardiac magnetic resonance (CMR). METHODS: Female patients with RA with active disease and healthy controls were enrolled. Cardiac symptoms were absent in all subjects. Tocilizumab (TCZ; 8 mg/kg IV every 4 weeks) was prescribed for patients with RA with an inadequate clinical response to methotrexate. All subjects underwent baseline evaluation of LV function and structure measured by CMR. We compared measures of LV geometry and function between patients with RA and patients without RA controls at baseline, and changes in the same variables between baseline and after 52 weeks of treatment among the group with RA. RESULTS: Twenty women with RA were compared with 20 women without RA of similar mean age. In patients with RA at baseline, ejection fraction (EF) was significantly lower (-3.7%) and LV mass index (LVMI) significantly higher (+9.2%) compared with controls. TCZ treatment resulted in a significant decrease in the Simplified Disease Activity Index (SDAI) after 52 weeks of treatment, paralleling a significant increase in EF (+8.2%) and a significant decrease in LVMI (-24.4%) over the same period. The percentage change in LVMI correlated strongly with the percentage change in SDAI (r = -0.63, p = 0.0028). LV geometry in the group with RA at baseline showed eccentric hyper-trophy compared with the group without RA, a condition that normalized after TCZ treatment. CONCLUSION: TCZ treatment significantly increased EF and decreased LVMI associated with disease activity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".