Induction Immunosuppressive Therapy in the Elderly Kidney Transplant Recipient in the United States
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The choice of induction agent in the elderly kidney transplant recipient is unclear. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The risks of rejection at 1 year, functional graft loss, and death by induction agent (IL2 receptor antibodies [IL2RA], alemtuzumab, and rabbit antithymocyte globulin [rATG]) were compared among five groups of elderly (≥60 years) deceased-donor kidney transplant recipients on the basis of recipient risk and donor risk using United Network of Organ Sharing data from 2003 to 2008. RESULTS: In high-risk recipients with high-risk donors there was a higher risk of rejection and functional graft loss with IL2RA versus rATG. Among low-risk recipients with low-risk donors there was no difference in outcomes between IL2RA and rATG. In the two groups in which donor or recipient was high risk, there was a higher risk of rejection but not functional graft loss with IL2RA. Among low-risk recipients with high-risk donors, there was a trend toward a higher risk of death with IL2RA. CONCLUSIONS: rATG may be preferable in high-risk recipients with high-risk donors and possibly low-risk recipients with high-risk donors. In the remaining groups, although rATG is associated with a lower risk of acute rejection, long-term outcomes do not appear to differ. Prospective comparison of these agents in an elderly cohort is warranted to compare the efficacy and adverse consequences of these agents to refine the use of induction immunosuppressive therapy in the elderly population.
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 | 0.001 | 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".