The value of extracorporeal photopheresis as an immunosuppression-modifying approach in solid organ transplantation: a potential solution to an unmet medical need
Bibliographic record
Abstract
Allograft rejection is a critical issue following solid organ transplantation (SOT). Immunosuppressive therapies are crucial in reducing risk of rejection yet are accompanied by several significant side effects, including infection, malignancy, cardiovascular diseases, and nephrotoxicity. There is a current unmet medical need with a lack of effective minimization strategies for these side effects. Extracorporeal photopheresis (ECP) has shown potential as an immunosuppression (IS)-modifying technique in several SOT types, with improvements seen in acute and recurrent rejection, allograft survival, and associated side effects, and could fulfil this unmet need. Through a review of the available literature detailing key areas in which ECP may benefit patients, this review highlights the IS-modifying potential of ECP in the four most common SOT procedures (heart, lung, kidney, and liver transplantation) and highlights existing gaps in data. Current evidence supports the use of ECP for IS modification following SOT, however there is a need for further high-quality research, in particular randomized control trials, in this area.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".