Antitumor activity of nivolumab on hemodialysis after renal allograft rejection
Bibliographic record
Abstract
BACKGROUND: Nivolumab (Opdivo™) is a novel IgG4 subclass programmed death-1 (PD-1) inhibiting antibody that has demonstrated breakthrough-designation anti-tumor activity. To date, clinical trials of nivolumab and other checkpoint inhibitors have generally excluded patients with solid organ transplantation and patients with concurrent immunosuppression. However, organ transplant recipients are at high-risk of development of malignancy as a result of suppressed immune surveillance of cancer. CASE PRESENTATION: wild-type cutaneous melanoma 10 years after renal transplantation. After downward titration of the patient's immunosuppressive medications and extensive multidisciplinary review, she was treated with nivolumab in the first-line setting. Within 1 week of administration, the patient experienced acute renal allograft rejection, renal failure and concurrent diabetic ketoacidosis due to steroid therapy. Allograft function did not return, but patient made a full clinical recovery after being placed on hemodialysis. Subsequently, the patient had clinical disease progression off therapy and required re-challenge with nivolumab on hemodialysis, resulting in ongoing clinical and radiographic response. CONCLUSIONS: This case illustrates multiple practical challenges and dangers of administering anti-PD1 immune checkpoint inhibitors to patients with solid-organ transplantation including need for titration of immunosuppressive medications, risks of allograft rejection, and treatment during hemodialysis.
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".