Lifetime Cost-Effectiveness of Calcineurin Inhibitor Withdrawal After De Novo Renal Transplantation
Bibliographic record
Abstract
After renal transplantation, immunosuppressive regimens associated with high short-term survival rates are not necessarily associated with high long-term survival rates, suggesting that regimens may need to be optimized over time. Calcineurin inhibitor (CNI) withdrawal from a sirolimus-based immunosuppressive regimen may maximize the likelihood of long-term graft and patient survival by minimizing CNI-associated nephrotoxicity. In this study, a lifetime Markov model was created to compare the cost-effectiveness of a sirolimus-based CNI withdrawal regimen (sirolimus plus steroids) with other common CNI-containing regimens in adult de novo renal transplantation patients. Long-term graft survival was estimated by renal function and data from published studies and the US transplant registry, including short- and long-term outcomes, utility weights, and health-state costs were incorporated. Drug costs were based on average daily consumption and wholesale acquisition costs. The model suggests that treatment with sirolimus plus steroids is more efficacious and less costly than regimens consisting of a CNI, mycophenolate mofetil, and steroids; therefore, CNI withdrawal not only shows potential for long-term clinical benefits but also is expected to be cost-saving over a patient's life compared with the most commonly prescribed CNI-containing regimens.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".