Post-transplant lymphoproliferative disorder and management of residual mass post chemotherapy
Bibliographic record
Abstract
INTRODUCTION: Post-transplant lymphoproliferative disorder (PTLD) is a rare complication. It represents a spectrum of lymphoid proliferations which occur in the setting of immunosuppression and organ transplantation. There are no reported cases or recommendations for the treatment of residual masses post rituximab of PTLD. PRESENTATION OF CASE: A patient with a long standing history of immunosuppression due to multiple kidney transplants starting in 1979, presented with a very large palpable hard abdominal mass (2004) after a fourth renal transplant. There was a past history of heavy immune suppression. CT scans revealed a conglomerate mass involving the right native kidney and two prior right sided renal allografts that crossed the midline. Biopsy of the large right retroperitoneal mass revealed large B cell lymphoma (CD 20 positive); consistent with post-transplant lymphoproliferative disorder (PTLD). DISCUSSION: Management of bulky PTLD, in a highly sensitized, heavily immune suppressed patient is not well described in the literature. The mainstay of therapy is IR and Ritixumab (R) monotherapy and combination R-CHOP. CHOP chemotherapy has an associated mortality rate of up to 38%. Radiotherapy is often considered over surgery and surgery has been most frequently used when associated with bowel complications. In this case report we describe upfront Ritiximab followed by consolidation resection and cytotoxic chemotherapy as a management strategy to reduce toxicity. CONCLUSION: The approach taken by our surgical team illustrates the benefits of disease debulking in certain cases of PTLD, by guiding further therapy and spacing and reducing chemotherapy in immune suppressed patients.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".