Switching Treatment Between Mycophenolate Mofetil and Azathioprine in Lupus Patients: Indications and Outcomes
Bibliographic record
Abstract
OBJECTIVE: To determine the reasons for changing treatment from mycophenolate mofetil (MMF) to azathioprine (AZA) or vice versa in lupus patients and to evaluate the effect of the change. METHODS: Lupus patients were identified from the University of Toronto Lupus Clinic database. Global disease activity in the 6 months prior to the change in therapy and 6 months after the change was calculated. The reasons for changing therapy were identified. RESULTS: One hundred eight switches occurred among 92 lupus patients: 89 switches from AZA to MMF and 19 from MMF to AZA. There was significant improvement in disease activity in the 6 months after drug switching compared to the 6 months prior to the switch when the reason was a drug failure. There was no statistically significant deterioration in disease activity in the 6 months after drug switching when the reason for the switch was a side effect, pregnancy, renal transplant, or financial. In the 19 patients who switched because of side effects, 15 (79%) had resolution of the side effects. CONCLUSION: Switching from AZA to MMF is most often due to AZA failure, whereas switching from MMF to AZA is mostly due to side effects and pregnancy. When the reason for the switch was drug failure, improvement in disease activity occurred and there was a reduction of steroid dose after 6 months. When the reason for switching was something other than drug failure, there was no deterioration in global disease activity. Switching for side effects usually resulted in elimination of the side effect.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".