Carpe diem—Time to transition from empiric to precision medicine in kidney transplantation
Bibliographic record
Abstract
The current immunosuppressive pipeline in kidney transplantation is limited. In part, this is due to excellent one-year allograft outcomes with the current standard of care (ie, calcineurin inhibitor in combination with anti-proliferative agents). Despite this success, a recent Federal government-sponsored systematic review has identified gaps/limits in the evidence of what constitutes optimal calcineurin inhibitor use in the short- and long-term. Moreover, recent empiric approaches to minimize/withdraw/convert from calcineurin inhibitors have come with the price of increased alloreactivity. As the time horizon to replace calcineurin inhibitors on a global scale may be distant, the transplant community should seize the opportunity to develop ways to personalize calcineurin inhibitor immunosuppression to the individual-transitioning from empiricism to precision. The authors argue in this viewpoint that the path to precision will require measures capable of detecting subclinical alloreactivity to define adequacy of immunosuppression, as well as novel genetic analytics to accurately define alloimmune risk at the individual level-both approaches will require validation in clinical trials.
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 | 0.020 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".