A Risk Score Using a Cell-based Assay Predicts Long-term Over-immunosuppression Events in Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Infections and cancer are major causes of premature death in organ recipients. However, there have been few advances in personalized immunosuppressive therapy. We previously reported that a cell-based assay measuring CD14 + 16 + tumor necrosis factor-α + monocytes after peripheral blood mononuclear cell (PBMC) incubation with Epstein-Barr virus peptides has a high sensitivity for detecting over-immunosuppression (OIS) events in kidney recipients in the short term. We aimed to develop a risk score for predicting long-term events. METHODS: We studied 551 PBMC samples from 118 kidney recipients. Following random allocation to a testing and training set, we derived a risk function for the delineated tertiles of low, intermediate, and high risk of OIS based on age and CD14 + 16 + tumor necrosis factor-α + cells. RESULTS: Patients were followed for a median of 6.3 y (25th-75th percentiles: 3.7-8.3 y). Of these, 40 (34%) experienced an OIS event. The validation indicated that the risk score was well calibrated, with an absolute risk of 21%, 41%, and 61% in the low-, intermediate-, and high-risk categories, respectively ( P = 0.03). In sensitivity analyses, the risk score was robust to alternative definitions of OIS ranging from mild to severe. In particular, when restricted to life-threatening OIS, the proportion of events varied from 5% to 27% among the low- and high-risk categories, respectively ( P = 0.01). CONCLUSIONS: Using a combination of age and in vitro PBMC response to Epstein-Barr virus peptides allows a substantial shift in the estimated risk of OIS events.
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".