Comparison of the long-term efficacy and safety of generic Tacrobell with original tacrolimus (Prograf) in kidney transplant recipients
Bibliographic record
Abstract
Abstract: This study aimed to evaluate the long-term efficacy and safety of a generic tacrolimus (Tacrobell [TCB]) compared to the original tacrolimus (Prograf [PGF]) in kidney transplant recipients. In this retrospective observational study, we analyzed the data from 444 patients who took TCB as a first-line immunosuppressive drug and 245 patients who took PGF. The 5-year graft survival rate was 92% for patients in the PGF group and 97% for patients in the TCB group, respectively. Cox proportional hazards for a one-sided, noninferiority model showed noninferiority (upper confidence interval [CI] limit of the hazard ratio [HR]<1.2) for TCB compared to PGF (HR: 0.58; 95% CI: 0–1.14). The 5-year patient survival rate was 96% for patients in the PGF group and 97% for patients in the TCB group. Cox proportional hazards for a one-sided, noninferiority model showed noninferiority (upper confidence interval limit of the HR<2.0) for TCB compared to PGF (HR: 0.83; 95% CI: 0–1.95). The 5-year acute rejection-free graft survival rate was not significantly different between the groups (TCB 67%, PGF 68.8%; P =0.6286). The incidence of adverse events including adverse cardiovascular or cerebrovascular events, malignancies, new-onset diabetes after transplantation, and infection events did not differ significantly between the two groups. We conclude that TCB is a comparable alternative to the original tacrolimus as a first-line immunosuppressive drug. Producers of generics should support further study of their products after approval to assure physicians of their efficacy and safety. Keywords: kidney transplant, generic, tacrolimus
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".