Experience with solid organ transplantation in patients with previous immunotherapy treatment is still limited but this is changing: The survey-based view of the global transplant society
Bibliographic record
Abstract
BACKGROUND: The use of immunotherapy for cancer is increasing and is expected to continue growing. The outcomes after solid organ transplantation(SOT) in patients who received immunotherapy before SOT remain unclear. We evaluated the global transplant surgery community's attitude towards and experience with patients who received immunotherapy for malignancy before SOT. METHODS: An online-based survey was sent to North American transplant program directors in December-2020 and members of the International Liver Transplant Society in November-2021 evaluating experiences with and attitudes towards SOT in recipients with previous immunotherapy for cancer. RESULTS: A total of 119 respondents completed the survey(119/175;completion rate:68%), representing centers from North America, South America, Europe, Asia, and Australia. Seventy-one(62%) respondents would consider SOT in patients with a previous history of immunotherapy for cancer, whereas thirty-nine(34%) were aware of such immunotherapy-treated recipients being transplanted, with an increasing trend over the last few years(2016[n = 1]-2020[n = 14]). Institutional clinical management policies in this setting were lacking in most centers(n = 85[75%]). CONCLUSIONS: The international transplant community is receptive to transplanting transplant candidates previously treated with immunotherapy for cancer, although experience is still limited. In this context, more centers have started to offer SOT to patients with a history of immunotherapy for cancer in recent years. However, support from clear and robust institutional policies in this endeavor is scant. Therefore, there is a high need for consensus guidelines to inform future clinical management, especially as immunotherapy for cancer is likely to continue to increase in the coming years.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".