Strategy to achieve biomarker-driven immunosuppression after solid organ transplantation by an academic-industry partnership within the European BIO-DrIM consortium
Bibliographic record
Abstract
Solid organ transplantation has emerged as the “gold standard” therapy for end-stage organ failure as it improves both quality of life and survival. Despite the progress in short-term graft survival, that is closely associated with the impressive reduction of acute rejections within the first year, long-term graft and patient survival remain almost un-changed and unsatisfactory. Incomplete control of chronic allograft injury but particularly the adverse effects of long-term immunosuppression, such as graft toxicity, diabetes, cardiovascular events, infections, and tumours continue to challenge the long-term success. In general, immunosuppression is applied as one-size-fits-all strategy. This can result in over- and under-immunosuppression of patients with low and high alloresponsiveness, respectively. Trial- and -error strategies to minimize or even completely wean of immunosuppression have a high failure rate. Consequently, there is an unmet medical need to develop biomarkers allowing objective risk stratification of transplant patients. To achieve this goal, we engaged in an academic-industrial partnership. The central focus of the European-wide BIO-DrIM consortium (BIOmarker-Driven IMmmunosuppression) is the implementation of biomarker-guided strategies for personalizing immunosuppression to improve the long-term outcome and to decrease the adverse effects and costs of chronic immunosuppression in solid organ transplant patients. The concept includes four innovative investigator-driven clinical trials designed by the consortium.
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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 | 0.073 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".