Preoperative Cylex assay predicts rejection risk in patients with kidney transplant
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: The ImmuKnow assay measures cell-mediated immunity by quantifying ATP release from CD4+ T-cells in peripheral blood. Herein, we hypothesized that this assay could predict complications associated with over-/under-immunosuppression in patients with kidney transplant (KT). METHODS: Sixty-seven patients undergoing KT were recruited prospectively and had ATP levels measured preoperatively, and at specified intervals over two months. Clinicians were blinded to ATP levels. Clinical events including rejection and infection/cancer were documented with a median follow-up of 21 months. Parameters including absolute ATP levels and changes in ATP patterns (slopes, delta) were analyzed. Association between ATP parameters and clinical outcomes was compared using the likelihood-ratio test and Kaplan-Meier curves. RESULTS: Absolute ATP values postoperatively had poor predictive value with regard to rejection or infection/malignancy. As well, changes in ATP values were poorly associated with complications. Importantly, patients with pre-transplant ATP values <300 ng/mL had significantly less rejection episodes vs. those with ATP values >300 ng/mL (p < 0.0001). CONCLUSIONS: For the first time, we have evidence that a preoperative ImmuKnow level can stratify patients with KT into low/high risk groups for rejection. Future studies used to assess the utility of this assay to design individualized immunosuppressive regimens are required.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".