Changing Paradigms in the Management of Rejection in Kidney Transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: P4 medicine denotes an evolving field of medicine encompassing predictive, preventive, personalized, and participatory medicine. Using the example of kidney allograft rejection because of donor-recipient incompatibility in human leukocyte antigens, this review outlines P4 medicine's relevance to the various stages of the kidney transplant cycle. SOURCES OF INFORMATION: A search for English articles was conducted in Medline via OvidSP (up to August 18, 2016) using a combination of subject headings (MeSH) and free text in titles, abstracts, and author keywords for the concepts kidney transplantation and P4 medicine. The electronic database search was expanded further on particular subject headings. FINDINGS: Available histocompatibility methods exemplify current applications of the predictive and preventive domains of P4 medicine in kidney transplant recipients' care. Pharmacogenomics are discussed as means to facilitate personalized immunosuppression regimens and promotion of active patient participation as a means to improve adherence. LIMITATIONS: For simplicity, this review focuses on rejection. P4 medicine, however, should more broadly address health concerns in kidney transplant recipients, including competing outcomes such as infections, malignancies, and cardiovascular disease. This review highlights how biomarkers to evaluate these competing outcomes warrant validation and standardization prior to their incorporation into clinical practice. IMPLICATIONS: Consideration of all 4 domains of the P4 medicine framework when caring for and/or studying kidney transplant recipients has the potential of increasing therapeutic efficiency, minimizing adverse effects, decreasing health care costs, and maximizing wellness. Technologies to gauge immune competency, immunosuppression requirements, and early/reversible immune-mediated injuries are required to optimize kidney transplant care.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".