Combination Therapy of Interferon Beta-1b and Tacrolimus: A Pilot Safety Study
Bibliographic record
Abstract
Tacrolimus is a calcineurin inhibitor which works to induce immune suppression by preventing cytokine transcription and lymphocyte activation. Combining the immunomodulator interferon beta-1b (Betaseron) with the immunosuppressant tacrolimus (Prograf) may have the potential of additive therapeutic benefit through the complementary mechanisms of action of these two therapeutics. In this randomized, open-label, multicenter, two-arm pilot study, the authors examined the safety and tolerability of the combination of interferon beta-1b and tacrolimus in relapsing remitting (RRMS) and secondary progressive (SPMS) multiple sclerosis patients who have failed one or more immunomodulatory therapies. Patients (n = 25) received a combination of interferon beta-1b subcutaneously every other day and oral tacrolimus (low blood level tacrolimus, 1-5 ng/mL, or high blood level tacrolimus, 5-10 ng/mL) for a period of 38 weeks. The combination therapy of interferon beta-1b and tacrolimus over the 10-month period of the study was shown to be safe and relatively well tolerated. There were no unexpected adverse events occurring as the result of the combination therapy. Further study of this combination therapy in patients with multiple sclerosis unresponsive to conventional therapy is warranted.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".