EFFICACY AND SAFETY OF TACROLIMUS IN THE MAINTENANCE TREATMENT OF PATIENTS WITH LUPUS NEPHRITIS
Bibliographic record
Abstract
PV125 / #23 Poster Topic: AS15 - Lupus Nephritis-Clinical Background/Purpose The treatment for lupus nephritis (LN) is associated with severe adverse effects and treatment failures. Calcineurin inhibitor such as tacrolimus has become increasingly interested as a therapeutic agent in LN. This study aims to evaluate the efficacy and safety of tacrolimus in maintenance treatment of patients with LN. Methods We retrospectively reviewed of medical records from the Ajou University Hospital included 179 patients who had biopsy-proven LN, with 92 in the tacrolimus and 87 in the non-tacrolimus. Clinical parameters were assessed at 6 months, 1 year, 2 years, 3 years, and 5 years. Complete (CR) and partial renal responses (PR) were defined based on established criteria. Adverse events, renal flares, and poor outcomes (ESRD or death) were documented. Results At 6 months, CR were 49.5% in the tacrolimus group and 56.6% in the non-tacrolimus group (p = 0.308). At 1 year, the non-tacrolimus group had a significantly higher CR rate (73.1% vs. 52.3%, p = 0.006), while the overall response rates were similar (p = 0.15). After 2 years, the non-tacrolimus group had higher CR rates (71.8% vs. 58.2%, p = 0.031) and higher overall response. However, at 3 and 5 years, the overall response rates were similar (75.3% and 72.9% in the tacrolimus and 83.1% and 85.5% in the non-tacrolimus, p = 0.252 and p = 0.1, respectively). Renal flare rates, poor outcomes, and adverse events showed no significant differences. Conclusions The efficacy and safety of tacrolimus in maintenance treatment have been demonstrated for patients with LN who have not achieved remission.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".