Induction Therapy in Elderly Kidney Transplant Recipients With Low Immunological Risk
Bibliographic record
Abstract
BACKGROUND: In nonimmunized patients, similar rejection rates are observed for patients who have undergone thymoglobulin (antithymocyte globulins [ATG]) or basiliximab (BSX) therapy. While ATG may improve delayed graft function, it may also be associated with higher infection rates and malignancy risk. We compared survival and clinical outcomes in elderly recipients with low immunological risk according to their induction therapy. METHODS: We conducted a multicentric study on nonimmunized patients ≥65 years of age receiving a first kidney transplant between 2010 and 2017. The principal outcome was patient and graft survival. Secondary outcomes were cumulative probabilities of infection, first acute rejection episode, malignancy, de novo donor specific antibody, posttransplant diabetes (PTD), cardiac complications, estimated glomerular filtration rate, and occurrence of delayed graft function. Cox, logistic, or linear statistical models were used depending on the outcome studied, and models were weighted on the propensity scores. RESULTS: Two hundred and four patients were included in the BSX group and 179 in the ATG group with the average age of 71.0 and 70.5 years, respectively. Patient and graft survival at 3 years posttransplantation were 74% (95% CI, 65%-84%) and 68% (95% CI, 60%-78%) in ATG and BSX group, respectively, without significant difference. Occurrence of PTD was significatively higher in BSX group (23% versus 15%, P = 0.04) due to higher trough levels of Tacrolimus on month 3 (9.48 versus 7.30 ng/mL, P = 0.023). There was no difference in other evaluated outcomes. CONCLUSIONS: In elderly recipients, ATG does not lead to poorer outcomes compared with BSX and could permit lower trough levels of Tacrolimus, thus reducing occurrence of PTD.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".