Bibliographic record
Abstract
Transplantation is an important and, in most cases, the only available therapeutic option for saving the lives of patients with end-stage organ failure. Furthermore, transplantation is recognized as one of the major medical achievements over the last half century. Despite outstanding short-term graft and patient survival rates, organ transplantation continues to face several major challenges. These include a severe shortage of donor grafts, poor long-term graft survival resulting from chronic vascular rejection, and major side effects from long-term immunosuppressive therapy required for prevention of rejection. Over the next several years, it will be imperative to develop novel solutions to these challenges in organ transplantation. Increasing the availability and quality of organs for transplantation will reduce patient death while on the waiting list and facilitate long-term graft survival after transplantation. Further understanding of the molecular mechanisms of cell activation has led to the discovery of additional new immunosuppressive agents that can now be used to allow individualization of immunosuppressive therapy. There is also a need to develop new strategies to monitor the use of immunosuppressive agents to reduce the incidence and severity of renal and cardiovascular toxicity and the increased incidence of cancer in transplant patients. Inducing a state of transplantation tolerance (graft acceptance without requirement for long-term immunosuppression) further offers the hope of improving the longevity of transplanted organs and the quality of life resulting from avoidance of significant side effects associated with immunosuppressive therapy.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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".