Cyclosporine/ketoconazole reduces treatment costs for nephrotic syndrome
Bibliographic record
Abstract
Cyclosporine A (CyA) is an effective agent for the treatment of glucocorticoid-dependent idiopathic nephrotic syndrome (GCDNS), but costs are prohibitive in resource-poor societies. The objectives of this study were to evaluate the efficacy and safety of reducing the dose of CyA by co-administering ketoconazole. A prospective study targeting children 2-18 years of age with GCDNS in remission with CyA monotherapy was conducted. CyA dose was reduced by 50% and ketoconazole was added at 25% of the recommended therapeutic dose, and the drug levels and therapeutic and adverse effects (AE) were monitored. Continued combined therapy after completion of the 4-week trial period was offered. Ten patients (median age 9.5 years, range 3.0-16.0 years) were enrolled in the study. At week 4, the CyA dose was 2.2 ± 0.7 mg/kg/day compared with 5.6 ± 0.9 mg/kg/day at enrolment (P < 0.0001). No AE were noted. All patients continued ketoconazole treatment for at least 3 months. CyA drug cost savings were 61%, and approximately 60% with ketoconazole cost included. The combination of an expensive immunosuppressive drug with a cheap metabolic inhibitor reduced the treatment costs by> 50% without increased adverse events or drug monitoring needs. This intervention demonstrates how access of patients with limited resources to needed drugs can be improved by interference with physiological drug elimination.
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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) | 0.000 | 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) | 0.001 | 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".