Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients with a failed kidney transplant represent a unique chronic kidney disease (CKD) population that is increasing in number, and that is at high risk of morbidity and mortality because of a prolonged history of CKD that may be sub-optimally managed, and exposure to immunosuppressant medications that are often continued after transplant failure. RECENT FINDINGS: There is no consensus on the optimal use of immunosuppressant medications after transplant failure. Recent observational studies have demonstrated that surgical removal of the failed allograft and discontinuation of immunosuppressant medications may be associated with a decreased long-term risk of mortality. However, the indications for elective transplant nephrectomy remain poorly defined. Removal of the failed allograft may limit opportunities for repeat transplantation by increasing cytotoxic antibody levels, and may be associated with an increased risk of repeat transplant failure. SUMMARY: In the absence of controlled studies, judicious use of immunosuppressant medications based on the patient's suitability for repeat transplantation, anticipated time to repeat transplantation, risk of sensitization, and drug tolerance, together with a cohesive plan for CKD management and appropriate preparation for dialysis, may improve outcomes in this unique patient population.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".