De Novo Donor-Specific Human Leukocyte Antigen Antibody Screening in Kidney Transplant Recipients After the First Year Posttransplantation: A Medical Decision Analysis
Bibliographic record
Abstract
Screening for de novo donor-specific antibodies (dnDSA) in stable kidney transplant recipients is routine practice in some centers. Patients with DSA are at increased risk of graft loss and early intervention may improve outcomes. However, the costs and benefits of dnDSA surveillance are unknown. A medical decision analysis to examine a screening strategy was developed for kidney transplant recipients who had stable graft function and were DSA negative 1 year posttransplant. In the base case, a modest 25% reduction in graft loss in dnDSA-positive patients treated with increased immunosuppression resulted in 0.04618 quality-adjusted years (QALYs) gained. However, benefits from reduced graft loss were eliminated if there was a small increased risk of death from added therapy. The incremental cost effectiveness was marginal at approximately $120 000-250 000 per QALY, but could be more or less favorable depending on several key variables such as efficacy of treatment, screening costs, incidence rate of subclinical dnDSA, and patient survival. Screening performed the best in patients with lower mortality rates and higher baseline incidence rates of dnDSA. Further study is warranted to gather the necessary high-quality evidence to justify screening.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".